{"as_of":"2026-08-07T06:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b2c0158367870684f3227083c4abefb9e60b4e1b56ecff5171ee227534e7d940","coverage":[{"denominator":113,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T16:19:07.949272Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2607.18072/citation-record","integrity":"/paper/2607.18072/integrity","json":"/paper/2607.18072/citation-record.json","paper":"/paper/2607.18072"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:18:02.313724Z","title":"SIGKDD , pages =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-01T16:18:02.313724Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:b0c87cce07a5ce90cc8951f6fdf2db57d0cd8df6be97372b59b9c6d0a4d61ea7","observation_id":"9d74ff59-9ef2-43ef-9ff8-a42fd1067001","resolution":{"observed_at":"2026-08-01T16:18:02.313724Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.397336Z","title":"Towards Concise Models of Grid Stability , booktitle =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.397336Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:9c709285c791a0b5f747b91c04803589fd5a003045d75d33c9e9462d2117054c","observation_id":"a82a5623-1593-4607-a675-05accb38543e","resolution":{"observed_at":"2026-08-01T16:19:07.397336Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.438182Z","title":"Multiclass classification of dry beans using computer vision and machine learning techniques , journal =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.438182Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:d09aa71c0fa52fb91021204b9eb5c723d82cf501578ccdbff6e71521078e8576","observation_id":"0675f8e6-fa26-4318-b944-905ec9ea4780","resolution":{"observed_at":"2026-08-01T16:19:07.438182Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.458056Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.458056Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:29b69d47e6d59f2cf9616bffa11eb87384252afb514b9bb712897610450004c5","observation_id":"459ff116-f627-40ae-abe1-99aae57fde4a","resolution":{"observed_at":"2026-08-01T16:19:07.458056Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.479295Z","title":"Challenges in benchmarking stream learning algorithms with real-world data , journal =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.479295Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:d79e6fb871c19448fb5adcd0bef7c2db1198171f431b538868f206a4f59f165c","observation_id":"781a0c26-c068-46ae-9240-38c36f7b2949","resolution":{"observed_at":"2026-08-01T16:19:07.479295Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.484311Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.484311Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:37e68b30ecdcdfac78ed19d1b1848f16fd463b65605fa158e0f7ff2072eb93bf","observation_id":"f86379eb-6080-4428-a6a7-f47b41b53fe5","resolution":{"observed_at":"2026-08-01T16:19:07.484311Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.492250Z","title":"Single-Domain Generalization in Medical Image Segmentation via Test-Time Adaptation from Shape Dictionary , booktitle =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.492250Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:b54f2a4eb70ef28411386c808c10f083632a49bd0882792cf0b68cf772603c07","observation_id":"3bf07703-63c6-4186-94bf-8dd2e1ecac03","resolution":{"observed_at":"2026-08-01T16:19:07.492250Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.498761Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.498761Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:7d17fb9c700014afdb49b76571ad89682d16569f67aafbbf803cd1597d9c35a6","observation_id":"3dd339f7-3291-436a-a281-09cb5ee8d30b","resolution":{"observed_at":"2026-08-01T16:19:07.498761Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.505340Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.505340Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:25cbe10ca02f921d23b3c4eb8f503bb06fb52d1a3daaeed398dd2fa280933e92","observation_id":"5216f18d-baf1-4bf7-82a6-c47edb823ece","resolution":{"observed_at":"2026-08-01T16:19:07.505340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.511826Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.511826Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:c117ec94d5e12be9935796d28592eb0834816cf61638f4e43b2a56a5738689ce","observation_id":"440e6634-5cc0-4761-bd39-c426f4e128c5","resolution":{"observed_at":"2026-08-01T16:19:07.511826Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.517788Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.517788Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:60b885361c7577f2679ea8b234a84490c033dbc2128627ad2df4237c4e7ffb96","observation_id":"53f3d4c0-f4ed-4534-8de4-173d266abc01","resolution":{"observed_at":"2026-08-01T16:19:07.517788Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.526682Z","title":"Modeling Tabular data using Conditional","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.526682Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:3709d90ef523f9d8769fe8b7b03570bf4e11af7c54f1ecbaa57bfbc48faf70e6","observation_id":"2ae73b5f-6df5-408a-ab6d-a24037d726b9","resolution":{"observed_at":"2026-08-01T16:19:07.526682Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.536507Z","title":"Conditional","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.536507Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:a352774d8c741c0750064ee37d8201e2a1c2d0cff043dfcca144b6c88fbff6e1","observation_id":"e0d1b6ed-4278-4e46-95c6-298d16a4358f","resolution":{"observed_at":"2026-08-01T16:19:07.536507Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.547502Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.547502Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:50e874aaec24ce27f62bc8b96c6cca73f1182b13600c920a337a4475f7ec5d4d","observation_id":"7c253889-9003-45f1-baa8-7feafdeaef20","resolution":{"observed_at":"2026-08-01T16:19:07.547502Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.10887","last_updated":"2019-10-28T10:49:54Z","snapshot_observed_at":"2026-07-06T07:55:38.610348Z","submitted_at":"2019-05-26T21:41:44Z","title":"Classification Accuracy Score for Conditional Generative Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.10887","snapshot_observed_at":"2026-08-01T16:19:07.559228Z","title":"arXiv preprint arXiv:1905.10887 , year=","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.559228Z"},"links":{"cited_paper":"/paper/1905.10887","citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:12525c960e285a9aa2da304cb1dd644ed3eb136ba336f106e438811ec3b93047","observation_id":"459c54f7-354d-4741-b688-693eca2c5291","resolution":{"observed_at":"2026-08-01T16:19:07.559228Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.14795","last_updated":"2022-09-25T06:07:53Z","snapshot_observed_at":"2026-08-01T20:34:23.629901Z","submitted_at":"2021-10-27T22:02:04Z","title":"MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.14795","snapshot_observed_at":"2026-08-01T16:19:07.566270Z","title":"arXiv:2110.14795 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.566270Z"},"links":{"cited_paper":"/paper/2110.14795","citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:f230cf99d68c596718ef0b8698c38ccbc79abc01b3738cd92437c6bfcd4e5624","observation_id":"d55dc45f-bbb7-4aef-bd3d-ad08444d47ed","resolution":{"observed_at":"2026-08-01T16:19:07.566270Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.572638Z","title":"Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.572638Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:c11172939d47f5cdbf7f058e7d799b046f1664bc57be0ed853469103bd8d3325","observation_id":"4f48a03f-d468-4992-838b-fa84ca2bdfd4","resolution":{"observed_at":"2026-08-01T16:19:07.572638Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.576575Z","title":"2020 , author =","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.576575Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:6b0c9020fa8bd823b7796fa4dd0fadac1f65c2d58d1ca6aeefe010b5d6742361","observation_id":"321a2d97-f4ff-42a1-ae2c-5982d1b356a5","resolution":{"observed_at":"2026-08-01T16:19:07.576575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.582911Z","title":"NeurIPS Workshop , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.582911Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:720719eaaddbddbed05f93a5d7a45dac66dbfc1a2f08000daec36a7ba933f07c","observation_id":"0299025a-a7d6-40d9-b510-93597a96b237","resolution":{"observed_at":"2026-08-01T16:19:07.582911Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1708.07747","last_updated":"2017-09-15T21:29:49Z","snapshot_observed_at":"2026-07-06T05:56:41.814255Z","submitted_at":"2017-08-25T14:01:29Z","title":"Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.07747","snapshot_observed_at":"2026-08-01T16:19:07.588182Z","title":"arXiv preprint arXiv:1708.07747 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.588182Z"},"links":{"cited_paper":"/paper/1708.07747","citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:ce53d6a1bc26fae7fd1165cd298558945cf07a1f45270c40e7fb3852bcdee916","observation_id":"a59230b8-7ee8-4824-9151-db4687715c8d","resolution":{"observed_at":"2026-08-01T16:19:07.588182Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.594494Z","title":"Proceedings of the IEEE , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.594494Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:48388f17df6cae04bd75e5d5a2c890dfcd481612278c8b6368ede70fe15375ba","observation_id":"8522d7a6-f565-4835-94d4-d494c9e1d49c","resolution":{"observed_at":"2026-08-01T16:19:07.594494Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.599201Z","title":"Understanding the Limitations of Conditional Generative Models , booktitle =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.599201Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:ef5d227fbc03f150f7229fa999dc320d6acc8056c3af4c283f2578b4fee6679e","observation_id":"65ed6979-1246-4117-be60-d7f74946749d","resolution":{"observed_at":"2026-08-01T16:19:07.599201Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.604917Z","title":"SIGKDD , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.604917Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:edc1ff22a2f76eec0a49ae57cc033e0bd7eb35ff5e9d7b8fcbfa3793c6e2f0df","observation_id":"c155ab55-34cb-4c2a-bcd2-2e0764695e90","resolution":{"observed_at":"2026-08-01T16:19:07.604917Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.612180Z","title":"Neurocomputing , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.612180Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:68184ed34279f763cd613f2df5ef55a47e1eef3a6a4e075f700c8c23b50249d4","observation_id":"55ada273-c216-4e63-98ed-594d3523eb92","resolution":{"observed_at":"2026-08-01T16:19:07.612180Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.617985Z","title":"ICCV , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.617985Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:f84bc68107c845946fab6d8bcb1724f5dfadfdea75f5986461300382e3145ac0","observation_id":"3ed00e07-4067-4829-84c6-ba51d6b7125e","resolution":{"observed_at":"2026-08-01T16:19:07.617985Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.622617Z","title":"ICCV , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.622617Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:8eb19936d698ebb86cbcc735611e9de3aaa4ce492b937635d0564cbb668d6352","observation_id":"7fb2a79d-6561-45c9-ae41-52159958ef68","resolution":{"observed_at":"2026-08-01T16:19:07.622617Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1701.00160","last_updated":"2017-04-03T21:57:48Z","snapshot_observed_at":"2026-07-06T05:24:40.838126Z","submitted_at":"2016-12-31T19:17:17Z","title":"NIPS 2016 Tutorial: Generative Adversarial Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1701.00160","snapshot_observed_at":"2026-08-01T16:19:07.628406Z","title":"arXiv preprint arXiv:1701.00160 , year=","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.628406Z"},"links":{"cited_paper":"/paper/1701.00160","citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:2a21b9695a85014d125d51a291e9ac91ecb0a6e5caf7fef41c0f9a9f12387d68","observation_id":"e42b29ba-ebb1-4ecf-ac91-a87aa644fede","resolution":{"observed_at":"2026-08-01T16:19:07.628406Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.07041","last_updated":"2021-12-13T22:03:40Z","snapshot_observed_at":"2026-07-06T12:18:21.983796Z","submitted_at":"2021-12-13T22:03:40Z","title":"Survey of Generative Methods for Social Media Analysis","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.07041","snapshot_observed_at":"2026-08-01T16:19:07.635533Z","title":"arXiv preprint arXiv:2112.07041 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.635533Z"},"links":{"cited_paper":"/paper/2112.07041","citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:d2c2e70cb3e84c341f4077d70d796f36bd03bae9756f161008fe0ab956265214","observation_id":"48249d86-490f-4069-9b36-86d7de3a5262","resolution":{"observed_at":"2026-08-01T16:19:07.635533Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.642074Z","title":"ACM Computing Surveys , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.642074Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:68172fa45df232a287c38746f2068172b68705f70cc93cc5c145bf6c36c67197","observation_id":"ab8f53c8-a8ad-4f20-81f3-ca00f7ca4a1b","resolution":{"observed_at":"2026-08-01T16:19:07.642074Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.651908Z","title":"ISCV , pages=","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.651908Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:2376975d949c81868857db00460658388a6823d90c48801f929059fa0ab084b9","observation_id":"319d555b-091c-4ff8-9efa-c7ae3334515e","resolution":{"observed_at":"2026-08-01T16:19:07.651908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.661248Z","title":"AI EDAM , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.661248Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:1e9c12dac40735fe142137bdce1f436ac6c7f0b128330f1e394c5c911dfc85fb","observation_id":"92f45f02-f9cb-4f5c-bf9a-7bc22bb169c9","resolution":{"observed_at":"2026-08-01T16:19:07.661248Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.675803Z","title":"ImageNet Classification with Deep Convolutional Neural Networks , volume =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.675803Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:f727f598fdd0de4c2545cb9d3fdeee68909ffed0ab5427e9506f86c0fbe42438","observation_id":"a70de847-4b5c-4ec6-822b-a92ad3bad037","resolution":{"observed_at":"2026-08-01T16:19:07.675803Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.682063Z","title":"and Karypis, G","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.682063Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:3debe19a64afcaadc3ba88b5c925d4792d61f9f6eda4bada9adc1fa618f93c5a","observation_id":"12f5bbbc-933e-455c-96f2-748db19a8f6e","resolution":{"observed_at":"2026-08-01T16:19:07.682063Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.686392Z","title":"Journal of the American Statistical association , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.686392Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:f8d037b5b0c0098cfec37fa25f5939737e9f9ffba30894e31cc3afcf446770f9","observation_id":"cf0d3729-5b8f-426c-b444-eb2cd6d57b1d","resolution":{"observed_at":"2026-08-01T16:19:07.686392Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.691313Z","title":"NIPS , pages=","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.691313Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:7f2edf180fddd7726654dde25091b9586ee8659e7eb10e60f33dcabfd986a824","observation_id":"f982a269-508f-48ac-85f4-3f96714297fd","resolution":{"observed_at":"2026-08-01T16:19:07.691313Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.696261Z","title":"Self-Tuning Spectral Clustering , booktitle =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.696261Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:0f41061d9e81e1a22bc0bdd187a2b3d1ce843e8ab209793a3d8db1e090c8a06a","observation_id":"42fd0795-f69c-4b71-a2c3-4ad5c064d35d","resolution":{"observed_at":"2026-08-01T16:19:07.696261Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.701958Z","title":"Cohen , title =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.701958Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:0e0034c5a3cf94accaf9e25c68084f4b89c22a06ac8f795bf5008a4a3e8e383a","observation_id":"24131f7c-ccea-4978-8e77-9f08ae5f2e68","resolution":{"observed_at":"2026-08-01T16:19:07.701958Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.708330Z","title":"2012 , school=","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.708330Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:bf5b3d96ee073cf069e0464755c965e1b42a995490ed5161044f9fa20808c6fa","observation_id":"f959821b-0931-4712-9fbb-e6c0e4482490","resolution":{"observed_at":"2026-08-01T16:19:07.708330Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.716297Z","title":"Applied intelligence , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.716297Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:4ca4d535f0468e3cb1374d115ae8dae528943885e6abec07aa72c2532b042ab3","observation_id":"4633ead6-fe68-48e0-a403-dd10e7fec128","resolution":{"observed_at":"2026-08-01T16:19:07.716297Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.731159Z","title":"WWW , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.731159Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:87466ac6962dc501aa43a52b667dbe3bc328ecda72948d77b3f924620c7d0e72","observation_id":"60878753-74fd-4baf-8616-d41ec8e8daac","resolution":{"observed_at":"2026-08-01T16:19:07.731159Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.737273Z","title":"SIGKDD , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.737273Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:9f4ef124c0d8eac94dc58486fe81d6937b48387eae96572b24d2fdfde542488e","observation_id":"c719bfaa-b5df-4b34-b60d-ba096c3fd3a7","resolution":{"observed_at":"2026-08-01T16:19:07.737273Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.744244Z","title":"IEEE Transactions on pattern analysis and machine intelligence , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.744244Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:f39291effa1dfe22420aa6fc7201dd57d566483e1a2f447aa0bfc935259622bd","observation_id":"bfb23bf6-950b-4daa-b181-9ce7e3d84d7f","resolution":{"observed_at":"2026-08-01T16:19:07.744244Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.749180Z","title":"NIPS , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.749180Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:61fe18f0ce6e4ef8e50ccec7ed2dded530ab521e87c613f56af38008482e3023","observation_id":"705de297-8f92-43ea-89b4-eb7216e43fbb","resolution":{"observed_at":"2026-08-01T16:19:07.749180Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.755328Z","title":"ICDM , pages=","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.755328Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:ab888a140203cecf44e8e803970d3962f6e642478b9ec4c57368ee8f527c6e3e","observation_id":"665be2fd-637b-4f37-bbb0-0b4735ef0e24","resolution":{"observed_at":"2026-08-01T16:19:07.755328Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.760612Z","title":"SIGKDD , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.760612Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:023b0ef103ec58b4b2ecbab3030c0be7d0a789e102def0f84a62cd21f13ac48f","observation_id":"24daa3ce-e3e9-40ed-9ae7-cc39713910e1","resolution":{"observed_at":"2026-08-01T16:19:07.760612Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.774175Z","title":"ICCV , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.774175Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:3e69c170ee70b71e43b7416124f6d3b1279905f4e3dbd873a2b4e41bb0399521","observation_id":"721aff7a-436c-4d5c-9103-bb59a50ca4ed","resolution":{"observed_at":"2026-08-01T16:19:07.774175Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.778759Z","title":"CVPR , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.778759Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:bde5f1d21f872174a691f809c92739137f8b99d3e85ed97d3dcd02ca2e2c684e","observation_id":"f8b1e3a9-c8a6-47f7-bd6e-7b856be7632b","resolution":{"observed_at":"2026-08-01T16:19:07.778759Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.781414Z","title":"The Journal of Machine Learning Research , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.781414Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:42a8fc4ec6d2cd6c17c5193a9e107c7344b69d5277d2e75248e59199b7985b15","observation_id":"71ccbd66-2631-4e99-9ec6-6d4115897a1c","resolution":{"observed_at":"2026-08-01T16:19:07.781414Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.784036Z","title":", author=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.784036Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:9b9e360942175d5c3c9ef9e4953db3a8fd305026b95960904908fc2b6fe668f6","observation_id":"44991c01-545b-468a-9dd0-0480c185fc78","resolution":{"observed_at":"2026-08-01T16:19:07.784036Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.786699Z","title":"ICML , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.786699Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:fb8866193a0d48d91289c8075a6bafb2b6cf0b5234160a7e1121f7e06e9a748e","observation_id":"c1390421-bf91-481a-8625-6539d13379eb","resolution":{"observed_at":"2026-08-01T16:19:07.786699Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.789921Z","title":"Pattern Recognition , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.789921Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:912c05fb563a36fdd113b6d8832f33c73077bf49deba29f316939be524af4f23","observation_id":"e7fa528e-b4d5-4e2e-b897-81181c149580","resolution":{"observed_at":"2026-08-01T16:19:07.789921Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.793275Z","title":"IEEE Transactions on Geoscience and Remote Sensing , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.793275Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:780045bec8bdcc6e9a66e58aae9665b974b7ba69c55c1c6fce42a06f1b63db5e","observation_id":"c7ad4c8d-dea6-4df3-b328-2c2a015ce44e","resolution":{"observed_at":"2026-08-01T16:19:07.793275Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.797010Z","title":"Neurocomputing , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.797010Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:1990a66987b35f9f44b1ef6245a80ee7984f4782694477c39e3597aad361cf59","observation_id":"5ee37895-3a1b-418a-ba09-0253639d871d","resolution":{"observed_at":"2026-08-01T16:19:07.797010Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.799846Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.799846Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:c259a017c86ccc399905f259d4acdd72499da7f97f0b2be8e4f2c5fff19af34c","observation_id":"a7829834-6acd-47a6-b4cb-ff54137d5954","resolution":{"observed_at":"2026-08-01T16:19:07.799846Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.802595Z","title":"CVPR , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.802595Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:4c8d922be4b65583fbf402de893377d12cb051f3888cd85109bb2f6080acdadb","observation_id":"627ca618-cef3-4c72-834e-65d1c9fffe89","resolution":{"observed_at":"2026-08-01T16:19:07.802595Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.805369Z","title":"Instance-Conditioned","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.805369Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:deb690643e444ca1da100b5f1ce5a8994e16f8aa2f99ba426cf9bdfbac2b299d","observation_id":"53ec377b-4d69-467d-aae9-36765f7616c8","resolution":{"observed_at":"2026-08-01T16:19:07.805369Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.808182Z","title":"CVPR , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.808182Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:36d56d51305394bcace95630d757abd4efe699a63a6030916951d313fdddad59","observation_id":"740f05a8-eed1-4bcd-9062-049440ed0f90","resolution":{"observed_at":"2026-08-01T16:19:07.808182Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.811273Z","title":"NIPS , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.811273Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:720d08e3aa221b510bfbb5589c334bddd0cdf83e859087fabd28074616f15ac2","observation_id":"fe98bf31-3d9d-47b4-af52-846173f333cc","resolution":{"observed_at":"2026-08-01T16:19:07.811273Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.814175Z","title":"ICML , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.814175Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:6a40796b1e0170aa5719115edb4fd81ed7588ce7a1ac82c4479c07528ada6a27","observation_id":"ea7d18ae-06ac-4c89-84b0-a12275310240","resolution":{"observed_at":"2026-08-01T16:19:07.814175Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.817027Z","title":"2021 , isbn =","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.817027Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:0984de5be5c35365993417b948b9f1b4a609c5db173a7de53b41ea256611ed18","observation_id":"7ef51de3-dfd4-4e64-be99-dbb6c4ba425f","resolution":{"observed_at":"2026-08-01T16:19:07.817027Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.820547Z","title":"NIPS , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.820547Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:cdeaff799ceda2c9725708c446914ddf1dc9e0a856259f5569e3c205bf4d6cd9","observation_id":"408f38c6-d463-4c17-97bb-a67f696f4757","resolution":{"observed_at":"2026-08-01T16:19:07.820547Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.823440Z","title":"Kingma and Max Welling , title =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.823440Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:5176173f54e92737cbe0d647926eab545aa13b2eb4ec005b4ec54e58c51472b8","observation_id":"a0b7509d-c70a-42a9-8e2b-506824d2a9c6","resolution":{"observed_at":"2026-08-01T16:19:07.823440Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1411.1784","last_updated":"2014-11-06T22:33:22Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-11-06T22:33:22Z","title":"Conditional Generative Adversarial Nets","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1411.1784","snapshot_observed_at":"2026-08-01T16:19:07.826173Z","title":"arXiv preprint arXiv:1411.1784 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.826173Z"},"links":{"cited_paper":"/paper/1411.1784","citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:6ed88527e58f86d1d1da0517d2663de2951b25c8bc0b466355968bfcb293ab4f","observation_id":"9f7e036c-c7a5-401a-837a-756f99fef3b8","resolution":{"observed_at":"2026-08-01T16:19:07.826173Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.829875Z","title":"NIPS , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.829875Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:8e4fd2011347a538dc5bb451df16ffdbc56e6f27ef4ab3c6ab368d1847c69135","observation_id":"2cf5de85-a9fe-4805-84e3-2171f5cf055c","resolution":{"observed_at":"2026-08-01T16:19:07.829875Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.832798Z","title":"Xing , title =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.832798Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:e97cc33667d1a920d59209f36f7c555cd6a55689b09b51eb60d1442d2b582ff3","observation_id":"09485423-54af-4744-be8f-c9fb72796a4a","resolution":{"observed_at":"2026-08-01T16:19:07.832798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.836122Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.836122Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:b0a1f83c165b3b5c96bcf391b913ed52aaf54c8cc1438bec0271e46e20557a61","observation_id":"508f450b-f20e-48f0-b573-ecb613c970cf","resolution":{"observed_at":"2026-08-01T16:19:07.836122Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.839350Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.839350Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:1dd027d514b746c2ea395c0395ce2e181236974815c99da0ea135de090a7be90","observation_id":"08e8defc-007a-47a0-a445-09e82b5b1a41","resolution":{"observed_at":"2026-08-01T16:19:07.839350Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.842621Z","title":"2022 , issn =","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.842621Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:c5ed6d92924522053a3bdfb9b56bb81d3d020b7cdbdb6ee632e75bbfdae63b2a","observation_id":"682f4d88-cbd7-42db-a884-03afd1c73168","resolution":{"observed_at":"2026-08-01T16:19:07.842621Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.07927","last_updated":"2025-08-11T08:03:06Z","snapshot_observed_at":"2026-08-06T17:45:23.292726Z","submitted_at":"2021-08-18T01:47:36Z","title":"Fed-TGAN: Federated Learning Framework for Synthesizing Tabular Data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.07927","snapshot_observed_at":"2026-08-01T16:19:07.845938Z","title":"arXiv preprint arXiv:2108.07927 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.845938Z"},"links":{"cited_paper":"/paper/2108.07927","citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:f6de272e2a7f930e4842cef568cf2111bb88ad1610dd733af1909a3ed19581a5","observation_id":"a77b2e15-2be4-42ca-a8bf-9576b638fa39","resolution":{"observed_at":"2026-08-01T16:19:07.845938Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.849142Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.849142Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:20128f3bd5e1ea2cf1669250733fe606b9fd1b33219ebd308359e3538d5d4198","observation_id":"2342bcd2-f866-4f21-9b57-7818d0edcff5","resolution":{"observed_at":"2026-08-01T16:19:07.849142Z","resolver_source":null,"status":"parse_uncertain"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.852342Z","title":"ICML , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.852342Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:b5940098b89265f6eeab67e3d124caf773de01aaba892dfec3bca3c2901999e2","observation_id":"f2497e9e-4237-4cac-a9c7-587ae8c60434","resolution":{"observed_at":"2026-08-01T16:19:07.852342Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.856240Z","title":"ICML , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.856240Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:6bfe89ef690c228c07aaeafde680f7f88a852f76104213827b553cf7374d779d","observation_id":"9ad6c995-654a-4cdb-bf6b-9e3942916e79","resolution":{"observed_at":"2026-08-01T16:19:07.856240Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.859142Z","title":"ICCV , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.859142Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:6363c6ca94cf19664f3ee341aff6a28510ea10df63755687ce5febd09dbe9035","observation_id":"1e6cd242-7fd4-41f8-85c5-53caecf6d018","resolution":{"observed_at":"2026-08-01T16:19:07.859142Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.862037Z","title":"CVPR , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.862037Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:adbb71fc0cd27dfccbee5a4d8e9c950c273118fe30e738d7227b50f141f1cea3","observation_id":"37cf3616-9181-426e-8f5f-c1d0afeb1172","resolution":{"observed_at":"2026-08-01T16:19:07.862037Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.864894Z","title":"WACV , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.864894Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:2e585661637ccf7f40a5c45f6a5efaf5daa223f5938f0dba1b6b994d2a6b3c16","observation_id":"fcfa33bf-4dc6-416a-8d61-5d9803c08a0d","resolution":{"observed_at":"2026-08-01T16:19:07.864894Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.867852Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":76,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.867852Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:78a6e87b6b21f63d2140a0a4ea1e4c414553d8a3eb404b068f06b242ad315825","observation_id":"3f653a7a-f895-4bf9-a2e8-991a546d2641","resolution":{"observed_at":"2026-08-01T16:19:07.867852Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.871741Z","title":", author=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":77,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.871741Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:9000a9ede3a737a26d5e5893b238b345a10b501dc39deb1b7842418ae7e88dda","observation_id":"26e18f16-508e-4dfc-a537-577328d7ebcb","resolution":{"observed_at":"2026-08-01T16:19:07.871741Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.874575Z","title":"IEEE TKDE , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":78,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.874575Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:a65dc548f43d18c1fc3e57bce5b92c61d58cbcbaec61e8250f014443d79a93e7","observation_id":"442f682f-226f-4ce1-aee4-888d2aede1d9","resolution":{"observed_at":"2026-08-01T16:19:07.874575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.877858Z","title":"Cell , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":79,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.877858Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:0c465b1a00576265bff2e800bbb12f57d48090bbfa65ebdbdbab97d75dab4c7a","observation_id":"5ef0d9b1-b178-4b2b-8132-b9f4e04eb2ad","resolution":{"observed_at":"2026-08-01T16:19:07.877858Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.881223Z","title":"Cytometry Part A , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":80,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.881223Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:f1000ad4f15187ffdb4fda1c766f083b9e27bb40c6e288493494158271443dec","observation_id":"6a67db5f-b37f-4143-8550-322d1b0b23bf","resolution":{"observed_at":"2026-08-01T16:19:07.881223Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.884734Z","title":"The 2nd diabetic retinopathy – grading and image quality estimation challenge , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":81,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.884734Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:459b0d221c2cbe46fefdefb6050bb2da49146b582c894b99f1efcf03ba662dab","observation_id":"d1e1b004-98be-47a3-9026-62c7629df20c","resolution":{"observed_at":"2026-08-01T16:19:07.884734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.888567Z","title":"NeurIPS , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.888567Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:1bd35ab770167be3c0fc6620fdfae764d48e26e16cb4c1a76e60f51829b11bfa","observation_id":"3a5a395f-c8e1-4c4d-973b-7861f0472509","resolution":{"observed_at":"2026-08-01T16:19:07.888567Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.892332Z","title":"NeurIPS , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":83,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.892332Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:42a4476aeed9a846afb8fe84a74b2810019f24a2eace2fbdc69b8d834406fd95","observation_id":"7665252b-ebb4-4b5e-8fe5-99f5eea9724e","resolution":{"observed_at":"2026-08-01T16:19:07.892332Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.895984Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":84,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.895984Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:fd3876e938873c2d042f3ad283bd788e2a92a65962e7d79dfe863fcbacd6c6e2","observation_id":"9654e287-c0df-45d9-9317-0485536b3fdd","resolution":{"observed_at":"2026-08-01T16:19:07.895984Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.899494Z","title":"NeurIPS , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":85,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.899494Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:91a99c95c65b40af731a61ab0e26ea0fa82238ccd6b7e8f4b2f34f6fb8db716b","observation_id":"af983d90-54ba-47f9-9dea-5f7ba9e9ff55","resolution":{"observed_at":"2026-08-01T16:19:07.899494Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.902007Z","title":"IEEE Transactions on Biomedical Engineering , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":86,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.902007Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:dd794727ec4ef7cd7aa7ecd3bc821dd56a951c71933728485bfd9af711a4f32e","observation_id":"ef655ee6-708c-4fdf-a32e-5787687fec44","resolution":{"observed_at":"2026-08-01T16:19:07.902007Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.905147Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":87,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.905147Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:56a89a1d686bc8162dd8931aae996cbca0a69583d7b8bacf6963514c7078baac","observation_id":"1ade758c-19f3-4129-95c2-8982c1fb6461","resolution":{"observed_at":"2026-08-01T16:19:07.905147Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.909092Z","title":"Clinical data sharing using Generative Adversarial Networks , volume =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":88,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.909092Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:a34c7e97a46636a5361352b8b7c1a991a4a3882d5c9c34476804fe10d1f7d03d","observation_id":"e6c86891-4127-48a6-8e28-08361ba3c086","resolution":{"observed_at":"2026-08-01T16:19:07.909092Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.912114Z","title":"CoRR , volume =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":89,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.912114Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:6eb0f11817d98cac50be4793562793644bc79908788afc72ae24b2931bc9a5e4","observation_id":"4c813f83-e56c-4701-9fe6-c6196826e714","resolution":{"observed_at":"2026-08-01T16:19:07.912114Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.915091Z","title":"CoRR , volume =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":90,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.915091Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:0f52654d74a60d823f53838dffeb0c72ee58807a83822e292d85c9cd93dc6258","observation_id":"3e6b2d03-96cc-46a5-85a7-332aeeb4ff3a","resolution":{"observed_at":"2026-08-01T16:19:07.915091Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.918293Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":91,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.918293Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:61a4a5614b916766bf74cb639419f17079894fb5c0ec893d4199662807dda2ce","observation_id":"f5a62eca-2ce7-4212-8ebd-ff8fae15f6f1","resolution":{"observed_at":"2026-08-01T16:19:07.918293Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.921379Z","title":"Diffusion Models:","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":92,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.921379Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:5107eecb0784a19ee2d2041fe97e8bb11407486dfec7f5391ad4f0eb08b07503","observation_id":"176af5d2-26ed-41ee-bb6a-0e422fd642df","resolution":{"observed_at":"2026-08-01T16:19:07.921379Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.924512Z","title":"Denoising Diffusion Probabilistic Models , booktitle =","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":93,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.924512Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:e785468883ea6c7a0e644136833b1190555c35d054098f1cd11b111434b8484c","observation_id":"75bfdb0f-a8eb-4569-ae39-08bbbc800893","resolution":{"observed_at":"2026-08-01T16:19:07.924512Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.930371Z","title":"Artificial Intelligence Review , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":95,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.930371Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:1e5a15bb1370214170affbb6574d4ae461616c546e22c5f7e36f9417cf2f7ccf","observation_id":"ad1ef5f6-ddf0-4d0e-8579-b7dc2ea3169a","resolution":{"observed_at":"2026-08-01T16:19:07.930371Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.933656Z","title":"IEEE Transactions on Pattern Analysis and Machine Intelligence , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":96,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.933656Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:9d4d6ba2cbfd5df3b79d741a517bc70988f30d6d6725340c085a2bb98d5cf38f","observation_id":"0a4b15d8-83d7-415d-9449-a3d77ae5dc79","resolution":{"observed_at":"2026-08-01T16:19:07.933656Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.937027Z","title":"Proceedings of the VLDB Endowment , volume=","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":97,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.937027Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:8794b4ae942286491baa6074713ca0d2ad96bfe52e4a099adab7a501ac2935df","observation_id":"3887747d-7871-4977-8acc-50d47b7651e9","resolution":{"observed_at":"2026-08-01T16:19:07.937027Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.940084Z","title":"International Conference on Machine Learning , pages=","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":98,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.940084Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:477a06f7255eb4a987120e53daeb5ba8717b5400f9c85b991c2c54a95a9eb2b9","observation_id":"e8ee8205-28f9-4948-8678-c248d0576a8b","resolution":{"observed_at":"2026-08-01T16:19:07.940084Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.943092Z","title":"Conditional GANs with Auxiliary Discriminative Classifier , booktitle =","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":99,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.943092Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:b74318469594e68133d657d172da4f3b803562871baea6fdc823aaf2956b9d54","observation_id":"25a159df-72a8-41f2-85e3-4423130d4252","resolution":{"observed_at":"2026-08-01T16:19:07.943092Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.946247Z","title":"2023 , url =","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":100,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.946247Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:6c7c3938d135a4f76b0387138f91cf19da56febb8df0f34c180ce612e4a8bcb0","observation_id":"7a997e91-8cd3-4bd9-b477-c3008d2f1f3e","resolution":{"observed_at":"2026-08-01T16:19:07.946247Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:19:07.949272Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift","version":1},"reference_index":101,"source":"arxiv_source","source_observed_at":"2026-08-01T16:19:07.949272Z"},"links":{"citing_paper":"/paper/2607.18072"},"observation_digest":"sha256:f67797c1810515f2e69d9465075f779a8b18a75cecdf797cb942a58cf707b65f","observation_id":"ccc96568-dfa3-4261-99b0-d2c39920fd18","resolution":{"observed_at":"2026-08-01T16:19:07.949272Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.18072","last_updated":"2026-07-20T15:40:51Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-04T10:53:16.421801Z","submitted_at":"2026-07-20T15:40:51Z","title":"SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":1,"unresolved":99,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":113},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 100 of 113 outbound references and 0 inbound Pith citation observations for arXiv:2607.18072."}