{"as_of":"2026-08-16T16:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:254ed08a3779070cf26d33bd1af0a9380de301f6b1ce079fad9107706c915b40","coverage":[{"denominator":72,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":72,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T11:16:49.232057Z","state":"measured"},{"denominator":72,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":72,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+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/2412.15668/citation-record","integrity":"/paper/2412.15668/integrity","json":"/paper/2412.15668/citation-record.json","paper":"/paper/2412.15668"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T11:16:50.349404Z","title":"Semantically coherent out-of-distribution detection,","venue":null,"work_id":"79751662-85fe-4761-9357-c13abb1e146c","year":2021},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:48.888422Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:4c64878968399e705d5c2cc8eb78d97db5865746dbc8d0e889f76dd5104c49ac","observation_id":"90d3982f-4d01-421b-84e2-f1f91a0d57d3","resolution":{"observed_at":"2026-08-11T11:16:50.354982Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:48.894340Z","title":"Learning multiple layers of features from tiny images,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:48.894340Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:3eb9f14bc5d944d394983d470c69fbc9d1e4ae941ea336ba2b969f20678500e7","observation_id":"1b8a0768-e546-4708-b762-baf5ee77e4ae","resolution":{"observed_at":"2026-08-11T11:16:48.894340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T11:16:48.899390Z","title":"Tiny imagenet visual recognition challenge,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:48.899390Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:d7f6d08134a4ac8c6fbbefaca4d42a1b8a3b2d2415d28ea84554f2831e822042","observation_id":"b7ffb0f0-8639-49a0-8509-b42f29dc33fa","resolution":{"observed_at":"2026-08-11T11:16:48.899390Z","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":"2026-08-11T11:16:50.315570Z","title":"Fine-grained food classification methods on the uec food-100 database,","venue":null,"work_id":"a1252c80-71da-4df5-9021-c61dee26147c","year":2021},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:48.904442Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:b3758944cd028e728199c8bebf9e7a3998ae7c2c52c46b17fdda98b77301faea","observation_id":"e4f38103-c5e7-419e-83f4-684e3b0baa8a","resolution":{"observed_at":"2026-08-11T11:16:50.320280Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:50.300111Z","title":"Garbagenet: a unified learning framework for robust garbage classification,","venue":null,"work_id":"082b60cf-63a4-4c4d-a5ab-93fea7d39434","year":2021},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:48.909019Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:d0369289a8d9d54c3fe448e7dcd2fdc691741673105b57c3f103947cd795ed16","observation_id":"4bc66234-55ba-4683-af5b-0a9607cfee0a","resolution":{"observed_at":"2026-08-11T11:16:50.305001Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:50.285871Z","title":"Approaches and applications of early classification of time series: A review,","venue":null,"work_id":"879758c0-5535-4585-a681-0ebbc9a6adfb","year":2020},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:48.913837Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:ac030f59eedd254cf9d5faaae6af5f8902c3cbf79e5fe3368f3a6d53bda9c9f0","observation_id":"6b37ea42-f873-4a62-bd78-2e5fe7545c61","resolution":{"observed_at":"2026-08-11T11:16:50.290592Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:50.270704Z","title":"Result- based re-computation for error-tolerant classification by a support vector machine,","venue":null,"work_id":"0e04552b-4119-4292-b4b2-ca56ccd6080e","year":2020},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:48.919084Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:4c35a28841bb6fbb42753135a8a76b50d5c1517bc3288c2df3fbfe77e8c593e0","observation_id":"12b22ac7-e6c4-4e03-a94c-b0cff2b1a92d","resolution":{"observed_at":"2026-08-11T11:16:50.275874Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:50.255036Z","title":"Multiscale repre- sentation learning for image classification: A survey,","venue":null,"work_id":"f7e6fd21-fb50-4261-bc82-01b4cab5b2e6","year":2021},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:48.923859Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:d61c9395c2eb7bc2a3a05f705c76af31e88f5ec0f93cff12aa1acaa09a6a9288","observation_id":"01ce1878-7b78-4aa4-b344-3a39244c0470","resolution":{"observed_at":"2026-08-11T11:16:50.260390Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:50.239821Z","title":"Open set domain adaptation: Theoretical bound and algorithm,","venue":null,"work_id":"bc3d3105-11d6-4327-85ae-5d8777cdac39","year":2020},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:48.928968Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:2e2379ed2498c700ffa65c1fa61b0ee65408d9e271700e7e45e1e9d6cf2e84de","observation_id":"bf29ffe4-c7f6-4e0a-b887-c3a42c2f5209","resolution":{"observed_at":"2026-08-11T11:16:50.244696Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:50.224637Z","title":"Uncertainty-aware op- timal transport for semantically coherent out-of-distribution detection,","venue":null,"work_id":"7de884d4-4cf4-48d8-9611-62fd201f29ac","year":2023},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:48.934482Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:f465ff527f7529880ac39bee147a4d01d2d0769415068cc37dff5f35cb06793b","observation_id":"0915fd5b-12c4-4e8f-aa43-410282f7f022","resolution":{"observed_at":"2026-08-11T11:16:50.229323Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:50.209429Z","title":"Character-level street view text spotting based on deep multisegmen- tation network for smarter autonomous driving,","venue":null,"work_id":"777a422e-d7b0-4051-82a4-463024e8a562","year":2021},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:48.939066Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:d976a9c62d4c44c3f175a03caadcc38b8bd5b46a0d5d37f19ced3bafcc84fb2a","observation_id":"8e511c98-3c52-4073-aade-27e0277fd88c","resolution":{"observed_at":"2026-08-11T11:16:50.214643Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:50.193195Z","title":"An overview of artificial intelligence ethics,","venue":null,"work_id":"8a34b76e-1c13-4bd8-b2a4-22636bf648ce","year":2022},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:48.943728Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:9c29944b44d4d81df047c3e6cbcd6cf99efcf15060ebeee82275773cc4730206","observation_id":"89962e12-41cd-4dd0-bc51-7e20a5f97907","resolution":{"observed_at":"2026-08-11T11:16:50.198181Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:50.175843Z","title":"Accelerating point-voxel representation of 3d object detection for automatic driving,","venue":null,"work_id":"7062d400-bb31-49d5-afa5-4bf96829be7b","year":2023},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:48.948667Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:5d0b2c63b1dbc554d99033a46ecd52171b9e8e8b476f560b3ab7b827486cd632","observation_id":"8e24bd75-df9f-4c2d-9189-90c066da80bc","resolution":{"observed_at":"2026-08-11T11:16:50.181805Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:50.160979Z","title":"A baseline for detecting misclassified and out-of-distribution examples in neural networks,","venue":null,"work_id":"4e8c672a-d045-4d40-bd31-37ac03dcdd1b","year":2016},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:48.953081Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:88bbaf76ace4f9805855d9bd632f2786a1552cfdf379efeda82db30491dbec0c","observation_id":"00cb9f0e-26e2-469b-9739-5f3ef18a2cbd","resolution":{"observed_at":"2026-08-11T11:16:50.165632Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:50.145366Z","title":"Improving calibration and out-of- distribution detection in deep models for medical image segmentation,","venue":null,"work_id":"31a10451-b846-4f64-bf35-2a5e08595ed5","year":2022},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:48.957824Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:62574ef38bdbef98f3c5cc7d7d2311d59bebecee27f7d7226e412c8248ebd55d","observation_id":"239ebb8b-494c-46f1-b76d-e372c13b2369","resolution":{"observed_at":"2026-08-11T11:16:50.151332Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:50.128633Z","title":"Rule- based out-of-distribution detection,","venue":null,"work_id":"32e47ca9-d3f5-4526-9712-a329d2bb38bc","year":2023},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:48.962105Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:38aad93a267ddf932fa1adfebf9c4b366e3dbda9f0d3742734f014f2a3c69126","observation_id":"d84d240c-ba0d-4bf3-9856-027248c6298c","resolution":{"observed_at":"2026-08-11T11:16:50.133786Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:50.112421Z","title":"An out-of-distribution attack resistance approach to emotion categorization,","venue":null,"work_id":"3ce95054-2e69-4d86-a11d-16c8633b0342","year":2021},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:48.966470Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:d4dbc7c8095296b370cc21f0ecef5be5bf99ceeeb09a9d7153a5d5b685d9b28f","observation_id":"02a77b96-ebb0-43aa-b4ca-040eafd38f3b","resolution":{"observed_at":"2026-08-11T11:16:50.117390Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:50.097405Z","title":"Few-shot learning network for out- of-distribution image classification,","venue":null,"work_id":"a0da86b8-dcb4-4082-a70d-385de7e28e8b","year":2022},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:48.970941Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:b71a98a3d6c360297c63df1b77c58af918ab7c5c1ffe55a2bbacca40da7828b9","observation_id":"34f3ea6c-e5f6-4ec8-9260-9661179e0380","resolution":{"observed_at":"2026-08-11T11:16:50.101936Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:50.082161Z","title":"Learning bounds for open- set learning,","venue":null,"work_id":"fa0498ef-840d-46ef-a651-9e7cff2b2c03","year":2021},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:48.975173Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:5f0713e808629d96d779e5f3b7948a7c25c068eafe5ce95f21f54cbf447a362b","observation_id":"8a441435-fbc4-4b8e-802f-8118578f73fb","resolution":{"observed_at":"2026-08-11T11:16:50.087561Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:50.066950Z","title":"Enhancing the reliability of out- of-distribution image detection in neural networks,","venue":null,"work_id":"42e45899-2a48-498f-9326-89af29cfb3e8","year":2018},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:48.980438Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:7041ee65f9b47ea613f9a6a66c1ad5e13878102b98d5b4555719ee76cfe6977b","observation_id":"340caf56-332f-4699-b4d7-b016660a121b","resolution":{"observed_at":"2026-08-11T11:16:50.071857Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:50.050990Z","title":"Energy-based out-of-distribution detection,","venue":null,"work_id":"4f5bd313-eede-4bcc-b3e5-34a17e78ce7f","year":2020},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:48.984953Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:b64ab6b3f5be8ce2f893268539ca7cc9884feb8846d1c4dece68611ad58b9acf","observation_id":"ace0d3fe-7ab8-4386-9fc6-c667d370c0f8","resolution":{"observed_at":"2026-08-11T11:16:50.056680Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:50.036734Z","title":"React: Out-of-distribution detection with rectified activations,","venue":null,"work_id":"12432602-3d7d-4d2d-bba8-969d30890cc2","year":2021},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:48.989077Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:906b45d4cdaa49970e1f868e0d07e1f21d387ec862abce51538bb4817d1d0bbe","observation_id":"8693ca03-26ca-4e57-9d2f-0e4ac70f4994","resolution":{"observed_at":"2026-08-11T11:16:50.041547Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:50.020585Z","title":"A simple unified framework for detecting out-of-distribution samples and adversarial attacks,","venue":null,"work_id":"ba3d7f92-51a6-499e-81d0-1290e68d7a2f","year":2018},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:48.993315Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:ced321da0823fa2347653fd7eb0a0559f1e742770b52d79d9d268e87921f610e","observation_id":"01c235c1-35a0-4118-9df0-bb5b54f70ba1","resolution":{"observed_at":"2026-08-11T11:16:50.026812Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:50.005753Z","title":"Self-supervised learning for generalizable out-of-distribution detection,","venue":null,"work_id":"dc136c15-61e1-4c55-aa6a-3163014eeb9e","year":2020},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:48.998430Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:fb0f54e2e8355c10d769ef4f50c483e38160aeaaa7a8193a5771444b4f15d735","observation_id":"7061f733-68d5-4f07-aa48-cdfe87924df4","resolution":{"observed_at":"2026-08-11T11:16:50.010571Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.990626Z","title":"Out-of-distribution detection using an ensemble of self supervised leave-out classifiers,","venue":null,"work_id":"ced6c7d1-fe2a-40e4-895a-b77075e05b54","year":2018},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.002883Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:34b01984532cd227981b3132e7d60651e8548825b319f7fa416bbb6dfa63c3b0","observation_id":"2d34de86-ad5b-440b-ad3a-e8a9a1928acb","resolution":{"observed_at":"2026-08-11T11:16:49.995415Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.974613Z","title":"Unsupervised out-of-distribution detection by maximum classifier discrepancy,","venue":null,"work_id":"452354d2-7b11-4dcf-8411-ad4f893bf15c","year":2019},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.007398Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:6324a7197ddcb8c7762ba267f16f26f94c66c84c7249442db42b9e01d3c378e3","observation_id":"ff2ab1b9-8d43-4f5a-a870-cd3f8b4d69a3","resolution":{"observed_at":"2026-08-11T11:16:49.979369Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.960132Z","title":"Out-of-distribution detection using union of 1- dimensional subspaces,","venue":null,"work_id":"52535238-b0c5-4836-8a45-ef54e21301ed","year":2021},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.012228Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:e5fe716888d1179194238d800920ceb6fd019f24c94a731b8cc0ffe4181d13ae","observation_id":"3f155bb0-3566-441b-8f90-c9a27d82a781","resolution":{"observed_at":"2026-08-11T11:16:49.964789Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.943478Z","title":"Generalized odin: Detecting out-of-distribution image without learning from out-of-distribution data,","venue":null,"work_id":"73264da9-61f4-45fc-8e4b-b223165f7bfb","year":2020},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.018041Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:1b3da8f6eb1c071fd2b1dd3b8c0e60cfa68a6671c086b8e59e2c2881d86322a0","observation_id":"16f8f657-f9e4-4edc-ac6c-4e8c23394c89","resolution":{"observed_at":"2026-08-11T11:16:49.949091Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.927928Z","title":"How to exploit hyperspherical embeddings for out-of-distribution detection?","venue":null,"work_id":"3c6816d1-f432-45b2-ba43-4b9b8a9c72f8","year":2023},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.022605Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:041ee41456884169b7aaef78e208c931df8bfc5923abfd592cda1bcd7972338b","observation_id":"2eccd372-cc93-4ba8-a8b9-e64fb0ca504b","resolution":{"observed_at":"2026-08-11T11:16:49.933259Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.911310Z","title":"Adversarial reciprocal points learning for open set recognition,","venue":null,"work_id":"15abbf79-c778-466a-906c-6e592cecbf28","year":2021},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.027193Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:6b93d49ed9d062498d19b3d71738fe4a2c71bd2de17475f08c458d272ad4bf5a","observation_id":"ae3c01cd-2f83-4bbc-b3de-b12d2be62052","resolution":{"observed_at":"2026-08-11T11:16:49.916291Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.896494Z","title":"V os: Learning what you don’t know by virtual outlier synthesis,","venue":null,"work_id":"5dfd7842-4fe6-4af1-a278-dcd3868d7f60","year":2021},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.032666Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:abb5223f4955783bfb955eb57214f51091e4a349c082887be8d89bf25cd97573","observation_id":"b376d085-014b-4b5b-a869-d0d897865229","resolution":{"observed_at":"2026-08-11T11:16:49.901602Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.879350Z","title":"Training confidence-calibrated classifiers for detecting out-of-distribution samples,","venue":null,"work_id":"efd86b8e-a7ea-46ca-a0ff-2c810aef59d7","year":2018},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.037336Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:a811ca1530bbdaea5d8dc3608f01f30a9ba792d8f8fa2557b74b5727db58e0b0","observation_id":"4feeca33-56c8-40d6-a471-07d7c7dbc56c","resolution":{"observed_at":"2026-08-11T11:16:49.884543Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.864591Z","title":"Building robust classifiers through generation of confident out of distribution examples,","venue":null,"work_id":"36680215-14b2-46c3-811b-432f80a28c82","year":2018},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.041848Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:c3f9c170507b7cb3d98294657d96eecc47d66de62cc7693df53d93c8ae86ecfd","observation_id":"2d28c013-7392-4364-b0c6-1d8f7cd8db31","resolution":{"observed_at":"2026-08-11T11:16:49.869464Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.04241","last_updated":"2019-10-09T20:38:56Z","snapshot_observed_at":"2026-07-06T08:28:13.180505Z","submitted_at":"2019-10-09T20:38:56Z","title":"Out-of-distribution Detection in Classifiers via Generation","version":1},"cited_work":{"arxiv_id":"1910.04241","doi":null,"metadata_source":"pith","pith_arxiv_id":"1910.04241","snapshot_observed_at":"2026-08-11T11:16:49.287768Z","title":"Out-of-distribution Detection in Classifiers via Generation","venue":"cs.LG","work_id":"46c2d9a2-470f-47cc-90bf-1ad869dc3a77","year":2019},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.046197Z"},"links":{"cited_paper":"/paper/1910.04241","citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:672727db038a10755e5e10bbcbd4e15f492feb27291efbc64bc5f9ef4795d59d","observation_id":"e3f7b7f5-981f-48d4-ad56-5f8a8ebbf7d8","resolution":{"observed_at":"2026-08-11T11:16:49.296015Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.848435Z","title":"idecode: In-distribution equivariance for conformal out-of-distribution detection,","venue":null,"work_id":"d54527a6-0362-4717-a085-20c0950da187","year":2022},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.051188Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:44d97a3ae0ef001fd2dd7b17188f3fdbd2d17131d5e7e5e7d220401324e077de","observation_id":"46df2c18-6093-44af-9e18-6acf701da6a9","resolution":{"observed_at":"2026-08-11T11:16:49.854265Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.832858Z","title":"Single layer predictive normalized maximum likelihood for out-of-distribution detection,","venue":null,"work_id":"c8e964e6-2a1d-47d5-b858-8015e4e2d20d","year":2021},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.057085Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:56e710597d7553d91baec095f1c8169ae8a492748c0290674679c49d4d34bd8b","observation_id":"2a8d3da9-7a87-4323-aed3-c54da4f25cff","resolution":{"observed_at":"2026-08-11T11:16:49.838057Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.818009Z","title":"Hierarchical novelty detection for visual object recognition,","venue":null,"work_id":"06488c4e-492e-4d16-bb1b-016ae2486b41","year":2018},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.061876Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:2489c83cb674c65dd4b6917752dc828d383f8d2a2277919e1b1c6e491f0d1744","observation_id":"a026c37e-816d-4f02-85a2-5c2373f80330","resolution":{"observed_at":"2026-08-11T11:16:49.822902Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.803098Z","title":"Why normalizing flows fail to detect out-of-distribution data,","venue":null,"work_id":"03706de5-5d5b-42f7-ad85-504d196a1dd4","year":2020},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.066300Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:1b2ddb4bc316fab5e4d747480b4577ec5d4417ea32f557167561dd1b2af4b76f","observation_id":"cd682604-d74d-4ef8-88f5-2a090e9a20b1","resolution":{"observed_at":"2026-08-11T11:16:49.807813Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.787085Z","title":"Input complexity and out-of-distribution detection with likelihood-based generative models,","venue":null,"work_id":"648debe9-3a1f-4246-9e34-17d82191030c","year":2019},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.070781Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:47a4a7a55415b4417915db61dc1af6e056ec893b11c7518889ee3bd4560700cc","observation_id":"449c5e6b-2140-4049-a20e-940f985ff879","resolution":{"observed_at":"2026-08-11T11:16:49.792307Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.770549Z","title":"Hyperparameter-free out-of-distribution detection using cosine similarity,","venue":null,"work_id":"80872f4a-ac4f-4759-ae05-0b4b4a69dd7d","year":2020},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.075214Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:e7d6005a00c873f3015c293674678a3d1d21d2902b14cb55ed08a8e55b918a88","observation_id":"eae84b05-c62f-400a-aa2a-def999a68ced","resolution":{"observed_at":"2026-08-11T11:16:49.775721Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.754261Z","title":"Rethinking reconstruction autoencoder-based out-of- distribution detection,","venue":null,"work_id":"f187d122-c49b-4b7e-8257-02032e999880","year":2022},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.079479Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:7c412d9fd66c97d98d66e77818c1da438c5f5b5350d073f7d2161e6a9e349ae3","observation_id":"09640456-d78a-48c4-bbfc-4a26d4a8aa46","resolution":{"observed_at":"2026-08-11T11:16:49.759971Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.737886Z","title":"Out-of-distribution detection with seman- tic mismatch under masking,","venue":null,"work_id":"4ac264b3-efc6-450a-bd2e-364c3c9138f2","year":2022},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.084013Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:a60d9ba28866a79072cb5044af8aa6bce89c7f62e95d30359a110dba2a064907","observation_id":"ffce7008-f014-43da-ac24-aa1f1721864b","resolution":{"observed_at":"2026-08-11T11:16:49.743176Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.721707Z","title":"Poem: Out-of-distribution detection with posterior sampling,","venue":null,"work_id":"4c32e0b5-b9a6-4523-ad3d-8755f9e9a1f9","year":2022},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.088266Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:bcffa9d02944f9f52789cff28e01ed5baf00719bc15821ad6a361155d772b36c","observation_id":"faa54d00-2c22-4e23-813d-a7fbb1376069","resolution":{"observed_at":"2026-08-11T11:16:49.727090Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.705713Z","title":"Conjnorm: Tractable density estimation for out-of-distribution detection,","venue":null,"work_id":"ab761a23-8629-497d-8e9e-9590d96a4904","year":2024},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.093129Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:d76ca74f457901baebc44d55a61747771d6fcb019ab5ca21ed0e9cd49116447c","observation_id":"6e40a694-238d-44c6-af8a-024e779e6c96","resolution":{"observed_at":"2026-08-11T11:16:49.711151Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.098490Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.098490Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:00733a35ef84b52f06f1327641911d9504937de2e0d728fe38f0eef4bc0c55c1","observation_id":"99369323-77c9-4fda-adef-991ed8685d69","resolution":{"observed_at":"2026-08-11T11:16:49.098490Z","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":"2026-08-11T11:16:49.680687Z","title":"Graph convo- lutional neural network for human action recognition: A comprehensive survey,","venue":null,"work_id":"c5068b32-2aae-4a38-a4e4-38ed01c77f8e","year":2021},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.103724Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:ff35334b0a37e17ebee972a4b880e3b87fb144910b9a1be0210f8076f65ef133","observation_id":"4470abcf-a224-4bcd-9c11-b70a7e893061","resolution":{"observed_at":"2026-08-11T11:16:49.685730Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.664600Z","title":"Prototype-based in- terpretable graph neural networks,","venue":null,"work_id":"b69481e8-ae36-4052-ba75-1198693d973e","year":2022},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.108259Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:37123679e0b26bc041a2afdcf25ceab6bf8f7c05655f46460c8d481c976d1fb6","observation_id":"1c5e6f43-6df8-4885-817f-3d6534e1640e","resolution":{"observed_at":"2026-08-11T11:16:49.669694Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.646577Z","title":"Multivariate time series representation learning via hierarchical correlation pooling boosted graph neural network,","venue":null,"work_id":"a0fa83d3-2d44-4249-aa38-e20dda44a19c","year":2023},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.112346Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:00df6ccca0d8e96a2ff21160d09f2093eec97abc91b5d50ee11b6dc0fca2410a","observation_id":"ffbae116-1edd-4a6f-afb0-f5db7e0f2662","resolution":{"observed_at":"2026-08-11T11:16:49.652486Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.628299Z","title":"Sparse vicious attacks on graph neural networks,","venue":null,"work_id":"e1210135-b251-4936-bf26-9ab207fde3c1","year":2023},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.117493Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:f7f6710d32936669b6fdcebb27c7e09d4cc0168970b7009e1f038b960f663163","observation_id":"9501b48e-8e6a-4051-bb29-5a0f8c37eb4f","resolution":{"observed_at":"2026-08-11T11:16:49.633573Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.612798Z","title":"A simple yet effective framelet-based graph neural network for directed graphs,","venue":null,"work_id":"8add28bb-fa92-4bbf-aecd-d43c1a1e61fa","year":2023},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.122242Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:38064173876cfc8c2bbce2e15333767b74215c408f096279ba34990bda185ff2","observation_id":"d3088b38-d24c-4c43-a883-e942bb646f00","resolution":{"observed_at":"2026-08-11T11:16:49.617908Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.597383Z","title":"Recognizing predictive substructures with subgraph information bottleneck,","venue":null,"work_id":"15924f8d-a5c2-4432-a365-708313f606f1","year":2021},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.127592Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:0319c86fedc180b9db219c35b51b78bbccb65b0b1f0c9985ca9220908e0bcbe6","observation_id":"8b35f9d6-c5bb-4404-84ed-337f1f1776f6","resolution":{"observed_at":"2026-08-11T11:16:49.602182Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.582204Z","title":"Spectral clustering with graph neural networks for graph pooling,","venue":null,"work_id":"09697451-bf6f-4c4a-b8e1-d448010cb0c2","year":2020},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.132657Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:fa9fe7de681493aa683ff88026c3bf96ecda215e464cc903695fc78caf278aa5","observation_id":"7946b368-35a4-4e50-ab0b-496ef0526b2a","resolution":{"observed_at":"2026-08-11T11:16:49.586960Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.567550Z","title":"Graph cuts in vision and graphics: Theories and applications,","venue":null,"work_id":"3c190e7d-002a-4ec4-ab75-390dcca2966a","year":2006},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.137312Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:a80acd149a789f2b15b6a3e30215c129af035eb058704cac25d1d46c52b22547","observation_id":"83c5d7d5-3e8a-40b0-9156-7dd3e359e8ef","resolution":{"observed_at":"2026-08-11T11:16:49.572451Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.142289Z","title":"Inductive representation learning on large graphs,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.142289Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:a39ef0b1879c497ee55d70f5526fa879df945eab4584360282242c6aa52f5cb5","observation_id":"cac649ab-b818-40da-9055-614b2a5efdb6","resolution":{"observed_at":"2026-08-11T11:16:49.142289Z","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":"2026-08-11T11:16:49.533487Z","title":"Deepcut: Unsupervised segmentation using graph neural networks clustering,","venue":null,"work_id":"ec88d934-cc4f-4904-845a-c295f79d3e58","year":2023},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.151617Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:2ab9481c897b2bbf6c2831fcadb9dde9742d46dbc59b7ae3cfbb2958fa40061b","observation_id":"0c10a79d-f76f-4913-bc73-266a161f361d","resolution":{"observed_at":"2026-08-11T11:16:49.538522Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.518662Z","title":"Atten- tion multihop graph and multiscale convolutional fusion network for hyperspectral image classification,","venue":null,"work_id":"20f978f6-5432-48ca-9d5c-99b998a04015","year":2023},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.156233Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:73524c39e650bb6564231933fb8dbc7efb1dbf732bde381f4b3912cf4ab2ce4f","observation_id":"cd0d4953-2976-4389-a099-25d4458777fe","resolution":{"observed_at":"2026-08-11T11:16:49.523615Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.502798Z","title":"Supervised hierarchical clustering using graph neural networks for speaker diarization,","venue":null,"work_id":"aa5a15c4-b9bc-45cf-862f-9680575c2e00","year":2023},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.160820Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:e21af297cb326376108fc9bfc27a67c187a271d97adfd7e0365ff85ba77ea2f6","observation_id":"22a3aca1-b4c3-48e7-a301-ec420f26d98a","resolution":{"observed_at":"2026-08-11T11:16:49.508058Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.485636Z","title":"Learning hierarchical graph neural networks for image clustering,","venue":null,"work_id":"2430abd1-e0eb-4145-8400-ad6eb2b480ce","year":2021},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.165358Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:0ab5a5ceb16050d3cf8fb234dc7b3fef9177112cfdb38eec742a5b0072f15468","observation_id":"0c925d27-4b98-4aff-aaf2-6da6e3b7fb11","resolution":{"observed_at":"2026-08-11T11:16:49.491123Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.470298Z","title":"Improving knowledge-aware recommendation with multi-level interac- tive contrastive learning,","venue":null,"work_id":"3b5aa329-4ad3-47e6-ac86-c276ba34eb3e","year":2022},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.170227Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:d341fc2a31184185b727e6d18c33ecaa346d2a5cdf6fb496ebc1300870960761","observation_id":"b4b51afd-4928-4b2d-92c4-a4808945d6f1","resolution":{"observed_at":"2026-08-11T11:16:49.475265Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.175283Z","title":"Graph attention networks,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.175283Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:9bece8e9683b71c41b97cc39ec76e6df7c3acb948557350f1d4de2a48bd92b16","observation_id":"3e969252-6700-409e-b5dd-25bb9dbc828e","resolution":{"observed_at":"2026-08-11T11:16:49.175283Z","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":"2026-08-11T11:16:49.453933Z","title":"Normalized cut-based saliency detection by adaptive multi-level region merging,","venue":null,"work_id":"548dec54-d4cf-4e96-87e8-44a19a96f681","year":2015},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.179710Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:28b7901c17bb27b80a06b75d63a35fbee2644f1e6f095b9bafc50aca6905f608","observation_id":"68b04df9-b3f6-4c2e-861b-1d3241a9dc6c","resolution":{"observed_at":"2026-08-11T11:16:49.459272Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.438822Z","title":"Graph convo- lutional network for multi-label vhr remote sensing scene recognition,","venue":null,"work_id":"2ee3b802-3b54-4266-9a1d-8cbcf813deda","year":2019},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.184397Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:cb6fdbe5bc2d25cdfeef4aef4b244d8c3044e80a45c0dc5d519d1006b43e5b95","observation_id":"899094b9-2efe-4a57-99f1-8869b7db44a2","resolution":{"observed_at":"2026-08-11T11:16:49.443820Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.423679Z","title":"Rosenblatt et al","venue":null,"work_id":"7770074b-2b90-4756-bb4e-18e4d5a43573","year":1962},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.188973Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:81a1af70ce586caee98371747078718bd4e5c4cba4b82a4da2248c1d4adab392","observation_id":"6f1fdef2-1c7c-4af1-bc87-1c9fa1b87f6c","resolution":{"observed_at":"2026-08-11T11:16:49.428643Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.193755Z","title":"Describing textures in the wild,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.193755Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:86068b363e7ef7c895c11e14b6b0becaac91566811fc58ab9128bbb299353284","observation_id":"9ede770a-bc59-4268-9b7c-7faf1f570245","resolution":{"observed_at":"2026-08-11T11:16:49.193755Z","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":"2026-08-11T11:16:49.395696Z","title":"Reading digits in natural images with unsupervised feature learning,","venue":null,"work_id":"3b842040-cc01-4f76-8683-e4314a813ced","year":2011},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.199267Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:16131d95efd5a7d74d18181432dc0027225d871e36da7d4233d16ea74f1a2a22","observation_id":"22cfe481-5aae-4380-9473-652eb547c934","resolution":{"observed_at":"2026-08-11T11:16:49.402097Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1506.03365","last_updated":"2016-06-04T09:51:30Z","snapshot_observed_at":"2026-08-08T13:26:17.517503Z","submitted_at":"2015-06-10T15:38:47Z","title":"LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1506.03365","snapshot_observed_at":"2026-08-11T11:16:49.204076Z","title":"Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.204076Z"},"links":{"cited_paper":"/paper/1506.03365","citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:87da1796a5e6bdfb106c31986f168c1fdd239c1569b92dc45b91587f6dfa1fee","observation_id":"1c8ff2e5-b589-4b53-b26b-5a7bb382b2c6","resolution":{"observed_at":"2026-08-11T11:16:49.204076Z","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":"2026-08-11T11:16:49.378183Z","title":"Places: A 10 million image database for scene recognition,","venue":null,"work_id":"781c7b02-1216-4e9a-bdb4-f70ddd815e21","year":2018},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.209246Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:d0136136ee621cd75e2888c0bf7266c196ac43bf43f00dcf70da1acb18b0bdde","observation_id":"53503c1d-409e-4b4f-9c91-6525a94ecbbc","resolution":{"observed_at":"2026-08-11T11:16:49.384129Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.363090Z","title":"Deep anomaly detection with outlier exposure,","venue":null,"work_id":"d2c40f52-cd75-481e-90dc-964408f1c0b1","year":2018},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.213818Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:6bd3a3dd00092014add66deea88c4fce447f3cd93def95e23b9d4c716c5e1aa0","observation_id":"1d040156-f2cd-48fd-826a-7d9a4ba1bc06","resolution":{"observed_at":"2026-08-11T11:16:49.367975Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.347770Z","title":"Feed two birds with one scone: Exploiting wild data for both out-of-distribution generalization and detection,","venue":null,"work_id":"a2180926-4383-4442-b312-13526b9f9439","year":2023},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.218177Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:ec70fbf6e25791d10c9c72b8b3e08bad254bd21730ab1cea6eefa5b9123b8db2","observation_id":"d571ee9f-8c70-4209-9009-12d977bdf360","resolution":{"observed_at":"2026-08-11T11:16:49.352662Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.332598Z","title":"Is out-of-distribution detection learnable?","venue":null,"work_id":"c189dc92-e046-433f-8816-8c9fc2c38fd0","year":2022},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.222831Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:e4a2e399933002cf137c79b79ff7a96f43a1097e64bd801cf6ae337901436caa","observation_id":"a5c93442-7ad3-400d-a0f6-f98c97b5a572","resolution":{"observed_at":"2026-08-11T11:16:49.337512Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.316875Z","title":"On the learnability of out-of- distribution detection,","venue":null,"work_id":"c9324160-c03a-4ce1-b609-5b6c0f6af006","year":2024},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.227465Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:85a2c403885885740c0830aade7626e6127e0fd9e5daeb0489737e1525fb930b","observation_id":"c5588be1-f706-4541-be93-80ea152fa25a","resolution":{"observed_at":"2026-08-11T11:16:49.322176Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T11:16:49.232057Z","title":"The graph neural network model,","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection","version":2},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-11T11:16:49.232057Z"},"links":{"citing_paper":"/paper/2412.15668"},"observation_digest":"sha256:e4740ea95e7eccccf3ddf424218a3cdd0d3141190c8be0b87f218cdcfd7f92e4","observation_id":"c5ed91ae-e0a5-4430-b2e0-9e5457f415a6","resolution":{"observed_at":"2026-08-11T11:16:49.232057Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.15668","last_updated":"2026-05-25T15:42:38Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-15T23:57:51.436519Z","submitted_at":"2024-12-20T08:32:02Z","title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection"},"reference_resolution":{"displayed":72,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":8,"verified_exact":1,"verified_fuzzy":63},"total_outbound_references":72},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 72 of 72 outbound references and 0 inbound Pith citation observations for arXiv:2412.15668."}