{"as_of":"2026-08-08T19:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b8d820af5039ccda2f7ae82b26688a76e96958ada78d8f3ef29ff35644f5be67","coverage":[{"denominator":69,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":69,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T04:09:43.431177Z","state":"measured"},{"denominator":69,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":69,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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/2506.11493/citation-record","integrity":"/paper/2506.11493/integrity","json":"/paper/2506.11493/citation-record.json","paper":"/paper/2506.11493"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:50.508681Z","title":"Analysis of representations for domain adaptation","venue":null,"work_id":"14c807aa-8513-4537-ac96-c05ca03e0ee4","year":2006},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:37.797649Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:c4a6d9feeb3aecc0e412820971dbc4112a74350b3cdb0e6d79921f980feaa34e","observation_id":"a4d46806-3def-47b3-9c37-025165842349","resolution":{"observed_at":"2026-08-07T04:09:50.515887Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:50.470769Z","title":"A theory of learning from different domains.Machine learning, 79: 151–175, 2010","venue":null,"work_id":"bf649fa0-4752-43f4-90c4-14aa01c2fa1d","year":2010},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:37.876703Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:12c39c469987e89cf021b33df6b4d3ea5a1b20da778a960a5f926a4089087d01","observation_id":"af3ec01a-7107-4cf3-a562-0374170133c3","resolution":{"observed_at":"2026-08-07T04:09:50.478160Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:50.444613Z","title":"Learning disentangled semantic representation for domain adaptation.IJCAI, 2019:2060–2066, 2019","venue":null,"work_id":"57a12d78-7617-4132-ba68-26d7362cdb2a","year":2019},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:38.009956Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:e34b56af0b3fc4161e9849c65481e6cd3c29841d16aec247e0e2e8860f0a1586","observation_id":"a63a8ae8-1aa3-4c64-bd09-d09cb284910c","resolution":{"observed_at":"2026-08-07T04:09:50.450484Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:50.415739Z","title":"Multi-prompt alignment for multi-source unsupervised domain adaptation","venue":null,"work_id":"56d72f6e-d3e5-49df-9ba4-8cc56bf55858","year":2022},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:38.155737Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:4be25f52d06935df2a9bbe6fa6b01994b6ec712de902081c60f27d740d46ccc2","observation_id":"1a5db1d0-078f-4899-9d58-172c11f55cb4","resolution":{"observed_at":"2026-08-07T04:09:50.422001Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:50.387199Z","title":"Transferability vs","venue":null,"work_id":"2a529548-5cb5-43e1-aa8f-fac0294ead1b","year":2019},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:38.305928Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:701faa2537e48f242a7b93666c381798ddf9eb73900546af2e6eb93b8b77661b","observation_id":"0712bb69-e856-4f61-800e-9cb1e82e6bb2","resolution":{"observed_at":"2026-08-07T04:09:50.396554Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:50.354571Z","title":"Towards discriminability and diversity: Batch nuclear-norm maximization under label insufficient situations","venue":null,"work_id":"f69ee43b-f209-4cb9-a708-df1566cb9a2a","year":2020},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:38.383318Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:2395119ddc45406777ef862056c5226c212ee64a530a116484a887d3a50c03a0","observation_id":"3fce2c3a-d0bf-42a0-8f34-e63d5d3b13a9","resolution":{"observed_at":"2026-08-07T04:09:50.362660Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:50.331805Z","title":"Gradually vanishing bridge for adversar- ial domain adaptation","venue":null,"work_id":"5221a719-b9e7-4eff-aaf8-17a6296d8bba","year":2020},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:38.473476Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:b13e7805ec20dcc6103fe847b94d31b4a7b4c65b8323e4d3fd186d4607a30ff9","observation_id":"eb8088d7-c50f-4231-950e-66cd424dc47b","resolution":{"observed_at":"2026-08-07T04:09:50.336806Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:50.299197Z","title":"Domain-agnostic mutual prompting for unsuper- vised domain adaptation","venue":null,"work_id":"e18cfd63-5323-4dfe-98b3-d17d017cc87f","year":2024},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:38.557914Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:48ae2b9d0cda98f191698c3947143ff29817d6e581ff7177410367f23158112d","observation_id":"ffa20d5c-5407-40b7-8403-6a4513aeda7a","resolution":{"observed_at":"2026-08-07T04:09:50.313067Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:50.270826Z","title":"Partial feature selection and alignment for multi-source domain adaptation","venue":null,"work_id":"23edcd78-4096-4821-9192-15e48e14c8d6","year":2021},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:38.634820Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:d054260cc25c664eca7ad2bdde2438d4b4f116b307a244baaa74a2dc209e48cf","observation_id":"73ee26f8-3b8c-4234-89b5-8a08f560ac4b","resolution":{"observed_at":"2026-08-07T04:09:50.278283Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:50.248729Z","title":"Stylegan-nada: Clip- guided domain adaptation of image generators.ACM Trans- actions on Graphics (TOG), 41(4):1–13, 2022","venue":null,"work_id":"a5c85695-e652-4020-a7fd-ccf3b1acc9b1","year":2022},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:38.715330Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:3f0a6716959b7db5610afa102bf68c56ac46d93464b08edd8343788ed1614507","observation_id":"77fda9de-3ca5-4961-8c07-3804c33d2800","resolution":{"observed_at":"2026-08-07T04:09:50.255170Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:50.209286Z","title":"Unsupervised domain adaptation by backpropagation","venue":null,"work_id":"be4ee07b-638e-4495-a1ea-ca55086092da","year":null},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:38.792380Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:c787dfca50d486573124825675d859fd3490c7c3c3def2fec349d53f37f7c357","observation_id":"93215950-cfcc-44a6-b9f0-41c57520bbf2","resolution":{"observed_at":"2026-08-07T04:09:50.217101Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:50.181336Z","title":"Domain-adversarial training of neural networks.Journal of machine learning research, 17(59):1–35,","venue":null,"work_id":"cf1fc7cc-e16f-4724-8228-7f3e287ba3e7","year":null},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:38.860363Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:d78b83695415b268f767d2a2498895feb9a4c24cd085f58b76356c86d390f1ae","observation_id":"753ab697-8342-4e6d-88ed-3510eab700ac","resolution":{"observed_at":"2026-08-07T04:09:50.188113Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:50.151071Z","title":"Domain adaptation via prompt learning.IEEE Transactions on Neural Networks and Learning Systems, 2023","venue":null,"work_id":"110454a2-aaae-4469-815c-e4315fe1fec6","year":2023},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:38.947530Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:6a61f54b8feca23b5ca925a16c16257f8a1393cca8a9ad7d5f193ea1bc1ccc44","observation_id":"02694157-4fad-439b-a97d-725033f78f5d","resolution":{"observed_at":"2026-08-07T04:09:50.161516Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:50.118066Z","title":"Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C","venue":null,"work_id":"411850dc-cbb6-41e1-86db-ca8a1cde4fb5","year":2014},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:39.016105Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:ca451c7bb9ffbf86c39eb19c5df94a4bcacb167485a5b5480bb4b4278f0573f6","observation_id":"17d3c679-bc35-4123-b390-b90e6fd7ea83","resolution":{"observed_at":"2026-08-07T04:09:50.126734Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:50.085355Z","title":"Rasch, Bernhard Sch¨olkopf, and Alexander J","venue":null,"work_id":"b980574f-568e-4640-9904-f171989e6030","year":2007},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:39.110471Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:547fdfbdf6bab3e001fd59cf4929d99cae913a4abff6cb8baac736066629bd3a","observation_id":"3937d309-4cad-4813-a86a-896bc4e18bc9","resolution":{"observed_at":"2026-08-07T04:09:50.092888Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:50.050727Z","title":"Spherical space domain adaptation with robust pseudo-label loss","venue":null,"work_id":"e80dda29-6553-4533-83d3-195278fefaea","year":2020},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:39.168985Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:e60b6f5a2906d128447b2326bc4dbd8d1808fb929463ce7e0535db5620f05619","observation_id":"2e05c1fa-1fe9-4e24-9d40-08c01eae787f","resolution":{"observed_at":"2026-08-07T04:09:50.059495Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:39.257889Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:39.257889Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:ff2d919030a58e86ab597e50c4aeb686999fa7f49201536ae56a7ff965400d2b","observation_id":"86376274-d186-401c-8369-219dda8f5049","resolution":{"observed_at":"2026-08-07T04:09:39.257889Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:50.006148Z","title":"Unsupervised domain adaptation with hierarchical gradient synchronization","venue":null,"work_id":"3d96bac3-b58a-4e74-9d3c-1c4e8f74061a","year":2020},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:39.333229Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:b4c992cef3101cf16f91ce3935fd22ae76038ee333016b2953faebe53f86fe07","observation_id":"6f6a3188-1dbe-41c8-ae1f-22555e1890fc","resolution":{"observed_at":"2026-08-07T04:09:50.014030Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:49.960369Z","title":"Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig","venue":null,"work_id":"8b0df059-8a95-4f9b-a739-127e8ff085b5","year":2021},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:39.382454Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:81c8f7091c1a442c3cbe4c579f8e0f805fc0279544be7bfe5238317f120dbe66","observation_id":"45ba509c-85cf-49c9-8b51-b1420f3684c9","resolution":{"observed_at":"2026-08-07T04:09:49.973893Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:49.929584Z","title":"Wilds: A benchmark of in-the-wild distribu- tion shifts","venue":null,"work_id":"d6d9729d-59ea-401d-9455-c22f62f84271","year":2021},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:39.469848Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:ca2f6dda4bfb29dd379f432035e0f415629502a270559009e5cb71b2e1ba0fa1","observation_id":"5f13124c-13d8-41c1-b229-d7cd3349130f","resolution":{"observed_at":"2026-08-07T04:09:49.936816Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:49.889160Z","title":"Padclip: Pseudo-labeling with adaptive debiasing in clip for unsupervised domain adaptation","venue":null,"work_id":"e04da8db-71c0-407f-b915-09b8aa2b8c6c","year":2023},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:39.535967Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:d4a63388a9dcdc828bef779bde1b02b638d70fd16240e2d579fe0584adb4f56b","observation_id":"75e7bba0-7b8a-4a1f-ade7-cf0e2a8bdc1a","resolution":{"observed_at":"2026-08-07T04:09:49.902533Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:49.866142Z","title":"Empowering unsupervised domain adaptation with large- scale pre-trained vision-language models","venue":null,"work_id":"638645e2-7e3e-4f2f-9ad4-f0d1bb66cf80","year":2024},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:39.626195Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:18eb9e6cc3abd16bd87605a9abb87fadfe833fe5570b5ef40ece34f6a1b6243f","observation_id":"26fc9305-ff5a-483c-aea6-cf0d5d431a71","resolution":{"observed_at":"2026-08-07T04:09:49.873492Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:39.692135Z","title":"Sliced wasserstein discrepancy for unsu- pervised domain adaptation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:39.692135Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:df78afe32f37ad48fe62bb938b5877f1e3e29dbf6907edcd3f9892a432a87d99","observation_id":"ebb694ca-ad49-4a75-a61e-204210223bd5","resolution":{"observed_at":"2026-08-07T04:09:39.692135Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:49.808142Z","title":"Enhanced transport distance for unsuper- vised domain adaptation","venue":null,"work_id":"8efcf3ba-fb47-47e1-aa47-e30e6f06341d","year":2020},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:39.767783Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:73a4283232dfac3d5047ce2508b03aa9bc517d948190aa5b18f6915d8d60e030","observation_id":"3f84203f-632a-4305-86e0-1fab8824f3cd","resolution":{"observed_at":"2026-08-07T04:09:49.819941Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:49.767753Z","title":"T-svdnet: Exploring high-order prototypical correlations for multi-source domain adaptation.ICCV, 2021","venue":null,"work_id":"5092a2c2-047d-4ab2-a127-1dc0d4e00421","year":2021},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:39.834122Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:8d9ebef559a42a7fdf24d6a9fd722022733e4549586b2d02d43aeb81b44603f9","observation_id":"d1f38e62-f99e-49bb-bda6-3c0b3a07de06","resolution":{"observed_at":"2026-08-07T04:09:49.775773Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02497","last_updated":"2024-08-29T10:12:35Z","snapshot_observed_at":"2026-08-08T04:55:15.737217Z","submitted_at":"2021-08-05T10:15:17Z","title":"How to avoid machine learning pitfalls: a guide for academic researchers","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.02497","snapshot_observed_at":"2026-08-07T04:09:39.895768Z","title":"How to avoid machine learning pit- falls: a guide for academic researchers.arXiv preprint arXiv:2108.02497, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:39.895768Z"},"links":{"cited_paper":"/paper/2108.02497","citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:6ad3a94d5141e6bd53e6ca30b026fdf6ea91252b3c00bdf7e2b8ecf5b31d3743","observation_id":"5c26e7ba-e351-4aca-8757-e82c9f11d74e","resolution":{"observed_at":"2026-08-07T04:09:39.895768Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:49.732291Z","title":"Learning transferable features with deep adaptation networks","venue":null,"work_id":"e584ec36-a8b6-4781-81b2-0f2e9664fed1","year":2015},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:39.966820Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:e22771ed8c26bafee2df77da82cd7a8384319a04f0adf45a0d5380c0178867f9","observation_id":"1fa57695-7055-4f4c-b1d2-b46a64f71c6c","resolution":{"observed_at":"2026-08-07T04:09:49.739575Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:49.681287Z","title":"Deep transfer learning with joint adaptation networks","venue":null,"work_id":"65b4b1c3-95bf-40a5-bed4-a423ebbce75b","year":null},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:40.040697Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:0b8c0e8ab3afa5fa3c9d2be24f57fb382441fe9a9dc030a5a618fde24b929667","observation_id":"321cc92b-d80f-404b-b618-3b28e6c0cd84","resolution":{"observed_at":"2026-08-07T04:09:49.689797Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:49.657935Z","title":"Transferable representation learning with deep adaptation networks.IEEE transactions on pattern analysis and machine intelligence, 41(12):3071–3085, 2018","venue":null,"work_id":"86190a6c-c5b2-4067-bcde-e5a20de64518","year":2018},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:40.134200Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:756879a44b9fdf7cf5d02015368b7691109a1329bb3640d5f56cb1b11d3cf5ed","observation_id":"7689edc1-603d-4f37-8528-90fd16406ffb","resolution":{"observed_at":"2026-08-07T04:09:49.663485Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:49.636813Z","title":null,"venue":null,"work_id":"d15109e1-0db9-48f6-90e8-744bd5db51bb","year":2018},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:40.189596Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:46f88845dbd2c8ef884eeb1da80a85b1519fa2e5a497e5ba8f6bb5b972b2f298","observation_id":"b466da48-63c8-496b-b62f-d8f1dd995be7","resolution":{"observed_at":"2026-08-07T04:09:49.642443Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:49.611733Z","title":"Tidot: A teacher imitation learning approach for domain adaptation with optimal trans- port","venue":null,"work_id":"edb6e25a-4858-46bb-8f68-691aeeefddbc","year":2021},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:40.276586Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:1d92f951bb08a8cb9c6272629873839a96571438e784b2356f4126c5464bfc6f","observation_id":"c34c5e82-8576-4685-8a6a-86205a3e05bb","resolution":{"observed_at":"2026-08-07T04:09:49.619885Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:49.592244Z","title":"Moment matching for multi-source domain adaptation","venue":null,"work_id":"73e259bf-dbbe-414e-a617-16186af83c17","year":2019},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:40.348734Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:8cb2a9a73ca6e24c5c0f2b10c3242c8d519f409c3978ced402d09a097f39cbd2","observation_id":"b7874775-0aa5-4a14-aec5-96b40b142c82","resolution":{"observed_at":"2026-08-07T04:09:49.597870Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:49.571381Z","title":null,"venue":null,"work_id":"e3b311c2-4be9-43e5-a7ac-1a7477defaa4","year":2019},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:40.405706Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:f0210c4111ee556f0446121ba4bf6338877df36fbc3f13fcc154a9160e10d2b0","observation_id":"5bf970a6-1318-4ca6-9432-d2350134ed03","resolution":{"observed_at":"2026-08-07T04:09:49.579751Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:49.547687Z","title":"Global-local regularization via distribu- tional robustness","venue":null,"work_id":"57d75510-3d9d-4610-a5d8-de70d4d1371e","year":2023},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:40.470991Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:ee20a79875d9a16926ac9fb3dab04d67102302cb567e119ab054880d39629f47","observation_id":"d3075834-604c-4d3b-9b53-44fd51e44ef8","resolution":{"observed_at":"2026-08-07T04:09:49.554581Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:49.520387Z","title":"Enhanc- ing domain adaptation through prompt gradient alignment","venue":null,"work_id":"4f1db491-8421-408b-8260-cb3388aa8251","year":2024},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:40.586929Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:bb6634f4fa5f8256991b2cc20b141d827010881ed6c8357298b2a0005621fcb3","observation_id":"6cd55256-d9e9-40e7-8db3-3fd23fc9d0d5","resolution":{"observed_at":"2026-08-07T04:09:49.527481Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:49.499607Z","title":"Control- lable prompt tuning for balancing group distributional robust- ness","venue":null,"work_id":"443c7597-5494-479b-8adc-0e861d5da107","year":2024},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:40.664890Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:e02db37971660e80857839da832c3030f345278120d64bdc6b72ed7f8518f9b5","observation_id":"2bca71a0-81a6-43d2-bc86-c8fb45fc70a1","resolution":{"observed_at":"2026-08-07T04:09:49.506370Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:49.480743Z","title":"Learning transferable visual models from natural language supervision","venue":null,"work_id":"ae23ad41-d2bf-437a-ac34-a110c0bd99e5","year":2021},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:40.764486Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:4f86d38a1805d26b48552cc862ccb559b78b7d1d87a29174eef8071507464da6","observation_id":"ee692b7f-a4fe-48e5-95bb-5722c4b6d28b","resolution":{"observed_at":"2026-08-07T04:09:49.485488Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:49.455380Z","title":"Multi-source unsupervised domain adaptation via pseudo target domain.IEEE Transactions on Image Process- ing, 2022","venue":null,"work_id":"5c836baf-8a7b-41e6-9c51-6cfeb78b93c4","year":2022},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:40.860112Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:fa112b20f8b50ae37e9340b1f3eb293711f43fc5325a3e132cc71f5fc5f79445","observation_id":"9497e4c4-43a9-41b2-bfd5-1fc86209946f","resolution":{"observed_at":"2026-08-07T04:09:49.462954Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:49.425435Z","title":"Distributionally robust neural networks","venue":null,"work_id":"fc12b164-f04c-40fc-b0c8-3389540dddeb","year":2019},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:40.924257Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:8ddeea0873ed2ac841b2fb22f8f05ee1c58d207274746ff1acafd469e5a80ca7","observation_id":"7603c451-6276-4db7-b407-2cc4bfc09b5e","resolution":{"observed_at":"2026-08-07T04:09:49.434349Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:49.325708Z","title":"Ushiku, and T","venue":null,"work_id":"a57adbef-9246-4854-b4eb-ca52d5bfeb9c","year":2017},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:41.034768Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:021003f1c9a49283b2a343552cc68c09ee1d415731da7599c1baf4736e9dee40","observation_id":"8d2412a0-3643-4e3d-92d5-f44c51a804b0","resolution":{"observed_at":"2026-08-07T04:09:49.384589Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1707.01217","last_updated":"2018-03-09T07:20:13Z","snapshot_observed_at":"2026-07-06T05:49:45.626127Z","submitted_at":"2017-07-05T05:34:13Z","title":"Wasserstein Distance Guided Representation Learning for Domain Adaptation","version":4},"cited_work":{"arxiv_id":"1707.01217","doi":null,"metadata_source":"pith","pith_arxiv_id":"1707.01217","snapshot_observed_at":"2026-08-07T04:09:43.613394Z","title":"Wasserstein Distance Guided Representation Learning for Domain Adaptation","venue":"stat.ML","work_id":"38f0e080-3240-467e-b34c-3660fcee8b6d","year":2017},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:41.158206Z"},"links":{"cited_paper":"/paper/1707.01217","citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:cf9ea7d03a6fb51d8f7267864e88267f22a2b135c3ae35d69d4ac85875ecbb89","observation_id":"7f2887ea-3028-4cf1-b9bc-1f9ec8671bb2","resolution":{"observed_at":"2026-08-07T04:09:43.764890Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:49.190075Z","title":"A dirt-t approach to unsupervised domain adaptation","venue":null,"work_id":"03c9a08b-1b82-4d84-a54a-d271072bea8f","year":2018},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:41.267096Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:93b4e74d28c04c304830a8da0cf2c96c91083aa137dacea9d7d46af59c416af6","observation_id":"33d6529c-8991-4a19-8d8a-26f3aaf40042","resolution":{"observed_at":"2026-08-07T04:09:49.232844Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:49.119410Z","title":"Deep coral: Correlation alignment for deep domain adaptation","venue":null,"work_id":"ad414134-76a2-43f4-8af7-7b839c60669f","year":null},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:41.390238Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:696e6722f47ca8a0f8a04429c617938af64146b61c458af6cc51be62b3c71f49","observation_id":"eb2f4f1e-770c-4608-821f-29ae540e30c6","resolution":{"observed_at":"2026-08-07T04:09:49.145078Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:48.988110Z","title":"Unsupervised domain adaptation via structurally regularized deep clustering","venue":null,"work_id":"d4005ccc-4656-4a3c-bf1a-a6d0f8f5caf9","year":2020},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:41.593120Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:66954899f607b9fd900a95830098a167543b8a40aa0187fca45374446bc924c2","observation_id":"0f22c2b8-2a8a-4688-b3b7-d145c2921a9a","resolution":{"observed_at":"2026-08-07T04:09:49.049791Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:48.831483Z","title":"Unsupervised domain adaptation via distilled discriminative clustering.Pattern Recognition, 127:108638, 2022","venue":null,"work_id":"4569dea7-7e14-403b-83ef-8ebd18c75cdd","year":2022},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:41.706233Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:397b3451e3a05c6e613f0c3d662a00cf017bef3f4d44a58d620ac43684238876","observation_id":"2e87e78c-af94-4779-9334-274f2a672351","resolution":{"observed_at":"2026-08-07T04:09:48.903035Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:48.718353Z","title":"Simultaneous deep transfer across domains and tasks.CoRR,","venue":null,"work_id":"fbd4cadc-58be-4424-b8a3-779cab978c2d","year":null},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:41.813997Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:10fee95c65095941b7543769af6cc9931a447bb44da845eac1eb94b725a1692c","observation_id":"247076b4-8496-4b8f-95b1-7234c7492405","resolution":{"observed_at":"2026-08-07T04:09:48.770886Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:41.941249Z","title":"Adversarial discriminative domain adaptation","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:41.941249Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:33d140176171cf775d0d02595c16062ff7d7054ea5acdcea91c9836dcb895368","observation_id":"05591cad-fefb-4dba-9eac-24c362afa340","resolution":{"observed_at":"2026-08-07T04:09:41.941249Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:48.562178Z","title":null,"venue":null,"work_id":"45398f03-3a7a-47b0-babb-6d2a9abcaca7","year":2021},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:42.065835Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:7a2c67a118871c48508bdb46de912be193739f2bc1324e0d6fa02b60b58138c0","observation_id":"b74a25f4-3d0b-4ce6-9c09-38db086b86c8","resolution":{"observed_at":"2026-08-07T04:09:48.633235Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:48.374942Z","title":"Deep hashing network for unsupervised domain adaptation","venue":null,"work_id":"e3724e1a-0c8a-4a6b-b245-4fd951063919","year":null},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:42.153123Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:9e57a8f12c294c2dc009818c64b17f11def86f039e625f4d746f8ced72fdbae7","observation_id":"b3dc0fd3-d1c0-4c22-a017-4be5c8286bbc","resolution":{"observed_at":"2026-08-07T04:09:48.452577Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:48.248288Z","title":"Springer, Berlin, Heidelberg, 2008","venue":null,"work_id":"14859dd8-1cf2-4087-9ef0-98ceda3b647a","year":2008},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:42.203698Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:bb4437f66a2984fbc2c61eb54d391957d07be21aea68206ce5ac16b8c3abeb9e","observation_id":"d51d8d43-8fa6-4d25-80c9-3fd79f01be41","resolution":{"observed_at":"2026-08-07T04:09:48.321134Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:48.152063Z","title":"Vector quan- tized Wasserstein auto-encoder","venue":null,"work_id":"0647e68a-df47-4334-8aa3-a03e635f475d","year":2023},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:42.259146Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:b7b3b2f3b124e4d895fe5fee32a36199b2ffa9fa69f9167f381ac0a87c7e28e8","observation_id":"b59095a9-fd4d-419f-9f66-284af8818990","resolution":{"observed_at":"2026-08-07T04:09:48.230104Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:47.901147Z","title":"Learning to combine: Knowledge aggregation for multi- source domain adaptation","venue":null,"work_id":"c52a92d5-0adb-46ad-ac53-00fb5cb8448c","year":2020},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:42.319384Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:6d5faffec5666008867e35664affe054b7ca8485fd5d58e30a176468f4456f49","observation_id":"d8996b8b-581c-4caf-a8dc-678a9521d665","resolution":{"observed_at":"2026-08-07T04:09:48.025622Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:47.621685Z","title":null,"venue":null,"work_id":"b04246ea-b0fc-4ac3-8831-b40f57434d7b","year":2020},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:42.366293Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:5134633cd54c8d7183ecb19426d6bf03113db438c691ab899cfd883a824fc1f1","observation_id":"32069ec9-1dc7-495d-a968-504230b07914","resolution":{"observed_at":"2026-08-07T04:09:47.789858Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:47.252279Z","title":"Zuo, Junjie Yan, and Liang Lin","venue":null,"work_id":"bc405b4f-34a2-4df6-8fe8-702c12342d3e","year":2018},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:42.426780Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:68f066e39a41c0a0cc7c2b0ad3355e4899b243aa146a5d10645692d06518295f","observation_id":"7c421cee-5aa7-4574-9fd5-8cf5c8b02204","resolution":{"observed_at":"2026-08-07T04:09:47.388305Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:47.040930Z","title":"How transferable are features in deep neural networks?Ad- vances in neural information processing systems, 27, 2014","venue":null,"work_id":"6a4f4aa4-b5bd-4087-95ff-2748a927184f","year":2014},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:42.474169Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:d385d25360f858a97e3e24e99bb2f8cfd115ab023deae977aef2d03253339863","observation_id":"36d9f97e-c623-4a06-9969-5a8d242a1f04","resolution":{"observed_at":"2026-08-07T04:09:47.143017Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:46.854756Z","title":"Autolabel: Clip-based framework for open-set video domain adaptation","venue":null,"work_id":"80895b23-a115-4b44-849f-4ad602acf1d2","year":2023},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:42.538265Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:287cf457375ba440e052191b254e39cda76a9ffe6d461368033b38a0656fdaa9","observation_id":"994d561a-08db-45d1-a0b5-7165d5996018","resolution":{"observed_at":"2026-08-07T04:09:46.965183Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:46.498102Z","title":"Domain- symmetric networks for adversarial domain adaptation","venue":null,"work_id":"85ff0639-052a-497d-811b-ec1a0d20bef1","year":2019},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:42.589788Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:4d394320ee8c17b03a7bde5c45efa8df2eadcd88f131dafd2de9d8b81756936b","observation_id":"1896f923-3c37-44ff-af0a-f78b7994bcfd","resolution":{"observed_at":"2026-08-07T04:09:46.664889Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:46.284248Z","title":"Costeira, Jos´e M","venue":null,"work_id":"c098fab8-c94c-46d4-8a82-5610ac4ee6fd","year":2018},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:42.643674Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:d02b3e58a0699ec6262a10e9b7dacadc86dc55ad38d6ecb996a9ab206ca632c0","observation_id":"59ad5163-33a9-47f6-a666-9190ecdfc8b8","resolution":{"observed_at":"2026-08-07T04:09:46.385647Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:46.029862Z","title":null,"venue":null,"work_id":"1f84ce96-0676-497c-bfbf-d81d40d8e8e5","year":2018},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:42.734420Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:06bec97c9ca51b4b4319e8ec923dc3701aacacf4c49bbde6c0d323b3a9c63e05","observation_id":"e5b5c9b7-a96a-4036-997e-d62719ee9514","resolution":{"observed_at":"2026-08-07T04:09:46.138032Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:45.815677Z","title":"Multi-source distilling domain adapta- tion","venue":null,"work_id":"f0dfb60d-e9be-4d97-8b69-82fc514eea89","year":2020},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:42.826403Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:3d7ac0268a0f1c329c904136e5feeceb90d732da2c7d7fdfc4f456379eff20c7","observation_id":"935d1b05-e397-42f4-94d9-952441eba7aa","resolution":{"observed_at":"2026-08-07T04:09:45.910859Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:45.536637Z","title":"Learning to prompt for vision-language models.Interna- tional Journal of Computer Vision, 130(9):2337–2348, 2022","venue":null,"work_id":"fe94aaa6-63b5-453d-9527-76e80c2dc4db","year":2022},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:42.915761Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:60d5f103f74db712fb4dd0c152066a1e4a91c7935025b1a372da0204c6fca433","observation_id":"acab9340-99b8-4d55-a340-2b55886d649f","resolution":{"observed_at":"2026-08-07T04:09:45.665594Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:45.341975Z","title":"Unsupervised domain adap- tion harnessing vision-language pre-training.IEEE Transac- tions on Circuits and Systems for Video Technology, 2024","venue":null,"work_id":"f9d0d226-7fbe-46f2-9a00-1058bbff1b0d","year":2024},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:42.990923Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:6f28afa9f570328efe610a53a796d04db89206a686c44e7791cda9c1848d263d","observation_id":"747b182b-76b7-45f9-80ed-3ba09488174c","resolution":{"observed_at":"2026-08-07T04:09:45.450958Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:45.088752Z","title":"Aligning domain-specific distribution and classifier for cross-domain classification from multiple sources","venue":null,"work_id":"413436a4-2bad-48a3-893c-680847abdb6a","year":2019},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:43.096310Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:129dd3994d381f6dcc82a4f042edbdd43c8eb877fc2a839f82d4dfe6e48c156c","observation_id":"c40329b9-6061-4e3b-9598-e051750f6cba","resolution":{"observed_at":"2026-08-07T04:09:45.188430Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:44.931034Z","title":"Office-Home is a medium-scaled dataset containing approximately 15,500 images from 65 categories in four do- mains: Art, Clipart, Product, and Real World","venue":null,"work_id":"28c0bda2-8b89-49a5-9435-3932f75ec2e7","year":2012},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:43.149725Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:3551adf12ec4febf4e60f58606f8041d49f85f4323e4bf47b5069b55542fc66b","observation_id":"e58a2ff5-8579-416c-8680-4af651214961","resolution":{"observed_at":"2026-08-07T04:09:44.978849Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:44.613677Z","title":"Since these alternative methods typically fine-tune many more parameters, we ex- clude them from the experiments to ensure a fair comparison","venue":null,"work_id":"86eff711-6b9c-49fb-9ab7-f39e753475e8","year":null},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:43.203659Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:68c29da371b7eadc5ad763eda52c1bd4c874adda6fcaa599ce085ba6f0f96ae6","observation_id":"8633a21b-e192-4185-9d27-b1d8edf2206c","resolution":{"observed_at":"2026-08-07T04:09:44.742287Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:44.374015Z","title":"Distance-Aware Pseudo-Label As discuss in previous section, different transferability be- tween domains motivate us a distance aware pseudo-labels scheme","venue":null,"work_id":"95366d1b-f2a0-4081-a12f-b92c02c464f1","year":null},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:43.270689Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:37a2e382ab055f9cc3d250269788413a48b06861701cd3f30f4e14e042bd1772","observation_id":"8d9ccdc1-dd03-4aa6-a688-fd9b2c8fe33e","resolution":{"observed_at":"2026-08-07T04:09:44.495312Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:44.172766Z","title":"Performance on Corrupted OfficeHome dataset","venue":null,"work_id":"6d495187-1530-48db-8fe3-6dc3f66cc3cd","year":null},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:43.361669Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:fb353d169a21cc430abb6d50564c75253178f87232ee18fbb5c120459d7ff4b2","observation_id":"9a9c6058-8388-491b-a08b-a391e2898e6f","resolution":{"observed_at":"2026-08-07T04:09:44.265943Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:43.942135Z","title":"Specifically, denote T={τ k T }K k=1 where τ k T represents the text embeddings of the context prompt [P k sh][P T ][CLASSk] for class k","venue":null,"work_id":"8b8f0eeb-7dbf-4f44-8d2d-cea870fbb578","year":null},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:43.431177Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:bbdcbd07945aaa10b9453f09fdbb3fd9c9e1785a548c864bfa13c5ff4cb22e20","observation_id":"fcc19fe9-02ca-4c23-8157-f75b1517ea7e","resolution":{"observed_at":"2026-08-07T04:09:44.019227Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:09:49.093453Z","title":null,"venue":null,"work_id":"c1117f23-4b77-469b-bd7f-37a8b5cb5dd6","year":2016},"citing_paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation","version":1},"reference_index":450,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:41.525594Z"},"links":{"citing_paper":"/paper/2506.11493"},"observation_digest":"sha256:c16599d2c21336ddfdfc39de0bfad917e940bbd93f2e204a19ad355e81254f75","observation_id":"a4ee69ce-4955-48a9-b05e-91d33f0b4be0","resolution":{"observed_at":"2026-08-07T04:09:49.099035Z","resolver_source":"raw_fallback","status":"parse_uncertain"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.11493","last_updated":"2025-06-13T06:33:27Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T04:02:18.244978Z","submitted_at":"2025-06-13T06:33:27Z","title":"Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation"},"reference_resolution":{"displayed":69,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":1,"unresolved":9,"verified_exact":1,"verified_fuzzy":58},"total_outbound_references":69},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 69 of 69 outbound references and 0 inbound Pith citation observations for arXiv:2506.11493."}