{"as_of":"2026-08-15T23:59:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8bd86a7078510b36f94bba8781a752c95b7b5db9757aab7cbcde4417d1307096","coverage":[{"denominator":59,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":59,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T13:37:40.135365Z","state":"measured"},{"denominator":60,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":60,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T20:46:17.208839Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"pith","source_observed_at":"2026-08-10T05:30:23.456663Z","state":"measured"}],"external_citation_measurements":[{"count":0,"observed_at":"2026-08-10T05:30:23.456663Z","source":"pith"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"cited_work":{"arxiv_id":"2411.16110","doi":"10.48550/arxiv.2411.16110","metadata_source":"pith","pith_arxiv_id":"2411.16110","snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","venue":"cs.LG","work_id":"21f2291c-ccc9-4e62-abb0-0f3c2e94b55a","year":2024},"citing_paper":{"arxiv_id":"2507.01924","last_updated":"2025-07-02T17:33:47Z","snapshot_observed_at":"2026-08-14T10:43:45.999914Z","submitted_at":"2025-07-02T17:33:47Z","title":"Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T20:46:17.208839Z"},"links":{"cited_paper":"/paper/2411.16110","citing_paper":"/paper/2507.01924"},"observation_digest":"sha256:e39f37127e763877fe38940476dc689298a71fe5e976577597cd5d1aa6b043bb","observation_id":"32229d58-ab22-45dd-a400-384be5bfd217","resolution":{"observed_at":"2026-08-06T20:46:22.170831Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2411.16110/citation-record","integrity":"/paper/2411.16110/integrity","json":"/paper/2411.16110/citation-record.json","paper":"/paper/2411.16110"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2310.17650","last_updated":"2023-10-26T17:59:19Z","snapshot_observed_at":"2026-08-13T05:39:36.069169Z","submitted_at":"2023-10-26T17:59:19Z","title":"A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly Detection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.17650","snapshot_observed_at":"2026-08-12T13:37:39.848439Z","title":"A coarse-to-fine pseudo-labeling (C2FPL) framework for unsupervised video anomaly detec- tion","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:39.848439Z"},"links":{"cited_paper":"/paper/2310.17650","citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:66555269873f05121ae7d572a6f0c944c0b7afb388ea479cf18f21166a1db8af","observation_id":"4060ff71-9ff2-4d8c-a841-98bb84213353","resolution":{"observed_at":"2026-08-12T13:37:39.848439Z","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":"2026-08-12T13:37:41.146810Z","title":"PNI: indus- trial anomaly detection using position and neighborhood in- formation","venue":null,"work_id":"d8755d64-4435-4799-ba24-c2d800070c44","year":null},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:39.854765Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:50720c691a8edf789d6e9a22ab83776d474b78715116707b12d7241edb8df4b9","observation_id":"06a1b833-87e2-4d55-a44d-936d8f029f8f","resolution":{"observed_at":"2026-08-12T13:37:41.151526Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:41.130334Z","title":"MVTec AD — A Comprehensive Real- World Dataset for Unsupervised Anomaly Detection","venue":null,"work_id":"d3b0f649-b541-4641-9b09-d2d85ebfb40c","year":2019},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:39.860199Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:a0ec751d1125322db1584317d5df83c948bdde29647bc9d63f7ae34ac68b89e5","observation_id":"148c0263-32c1-4c75-a53b-c91745e468c0","resolution":{"observed_at":"2026-08-12T13:37:41.135872Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:41.114408Z","title":"Lof: identifying density-based local outliers","venue":null,"work_id":"92bf2835-96ea-464e-93b6-c103a33397c0","year":2000},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:39.865680Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:78eadc93ee8016e5fda09bcc06b37d4920474517e29691f24817a6a346a545b6","observation_id":"d69d960e-655d-48e6-9510-c506ec9d8020","resolution":{"observed_at":"2026-08-12T13:37:41.119204Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:41.099613Z","title":"Emerg- ing properties in self-supervised vision transformers","venue":null,"work_id":"612116f2-2c52-4558-ad75-3502ae6b44bd","year":2021},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:39.870714Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:18ed397d1e4af12943fdc501c9bccaa2ea371b4ccbcfdcb2cbda96c91db4c13c","observation_id":"2f83c314-3b46-4e07-8225-676874a60747","resolution":{"observed_at":"2026-08-12T13:37:41.104352Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1901.03407","last_updated":"2019-01-23T06:26:15Z","snapshot_observed_at":"2026-08-14T17:32:27.775613Z","submitted_at":"2019-01-10T21:36:57Z","title":"Deep Learning for Anomaly Detection: A Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1901.03407","snapshot_observed_at":"2026-08-12T13:37:39.876176Z","title":"Deep learn- ing for anomaly detection: A survey","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:39.876176Z"},"links":{"cited_paper":"/paper/1901.03407","citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:723bceff8fb38164ec9a54bf554014af0d3d0b525fbc1d129423583f9823f44d","observation_id":"20b0e1df-6bfa-4ebc-b462-ec729dd2daba","resolution":{"observed_at":"2026-08-12T13:37:39.876176Z","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":"2026-08-12T13:37:41.084146Z","title":"Deep one-class classification via interpolated gaussian descriptor","venue":null,"work_id":"6e349458-35a9-4cf9-883c-cd10775c5701","year":2022},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:39.881461Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:85f97ddb34f8730519aaa5aad70d7f7a025966cb44fbe0ec0739f9bbed925c21","observation_id":"d30ce2d9-402b-40c4-ba2d-4ebf5925e409","resolution":{"observed_at":"2026-08-12T13:37:41.089156Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.02357","last_updated":"2021-02-03T16:28:51Z","snapshot_observed_at":"2026-08-13T23:57:38.237254Z","submitted_at":"2020-05-05T17:43:35Z","title":"Sub-Image Anomaly Detection with Deep Pyramid Correspondences","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.02357","snapshot_observed_at":"2026-08-12T13:37:39.887501Z","title":"Sub-image anomaly detec- tion with deep pyramid correspondences","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:39.887501Z"},"links":{"cited_paper":"/paper/2005.02357","citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:1887b432892707d06341738a90fb4251c49e8df21182511e6d2c89c6f6d5f0a2","observation_id":"26205791-3f92-43d6-aa86-73f81c7451e7","resolution":{"observed_at":"2026-08-12T13:37:39.887501Z","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":"2026-08-12T13:37:41.068973Z","title":"PaDim: a patch distribution modeling framework for anomaly detection and localization","venue":null,"work_id":"91069925-cb12-431c-9900-0a97ff27fb08","year":2020},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:39.892419Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:b78a3844bc3b8e365ce3557a1b17ec2f7318b411c3037992e34e79fa21cd8679","observation_id":"0844fe81-3628-4a61-a87c-5f10dae608d4","resolution":{"observed_at":"2026-08-12T13:37:41.073757Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:41.053354Z","title":"Anomaly detection via reverse distillation from one-class embedding","venue":null,"work_id":"d49122d1-8486-49a9-a007-60daf8502fb6","year":2022},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:39.897148Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:8a4d14d865acbd3ddd60558966465ca3d947901a1f68909196d52e416c64271c","observation_id":"56e18d92-a653-4287-9f47-caf2ba1886c6","resolution":{"observed_at":"2026-08-12T13:37:41.058686Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:41.038221Z","title":"Catch- ing both gray and black swans: Open-set supervised anomaly detection","venue":null,"work_id":"cf6a873b-a58d-48bf-8330-e622df0f99f8","year":2022},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:39.901805Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:db28079956852f7e87556db6af6dc063dda174569b11565cc7a67c7fa3678773","observation_id":"b6e5338d-2351-421b-95e9-aa530202d5af","resolution":{"observed_at":"2026-08-12T13:37:41.043062Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:41.022757Z","title":"An image is worth 16x16 words: Trans- formers for image recognition at scale","venue":null,"work_id":"48a8fab3-c7a7-4907-b06e-8e68c3a76f7d","year":2021},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:39.906844Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:96176cf446b5def584b2aa84e0dffcee53967e4be42b5bcb8a4ecaff8c340901","observation_id":"5fc3bc49-0a0d-4dc4-9f3d-7ff848bc4929","resolution":{"observed_at":"2026-08-12T13:37:41.027823Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:41.006447Z","title":"Robust anomaly detec- tion and backdoor attack detection via differential privacy","venue":null,"work_id":"e3d0e92c-95de-461e-bdb1-2796283b0ec1","year":null},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:39.911372Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:79a62ef0a9f25f762be9231f2a4e9fc4e5a90bb17de833a8007cc28d2a230a6f","observation_id":"b684ae15-f106-4175-9d47-be8cd22b7cb9","resolution":{"observed_at":"2026-08-12T13:37:41.011463Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.990508Z","title":"MIST: Multiple instance self-training framework for video anomaly detection","venue":null,"work_id":"1c416595-d7ab-4af9-9b64-f80d1a9c6939","year":2021},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:39.916267Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:5c8efaa9ddc917cdc9f8e2876d9a40b26c8b2be25c6e491da449c0a6d46f9e8c","observation_id":"81db353d-e959-417d-b1f3-3416abf2dbea","resolution":{"observed_at":"2026-08-12T13:37:40.995276Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.974582Z","title":"Ro- bust Loss Functions under Label Noise for Deep Neural Net- works","venue":null,"work_id":"4fefe14f-9368-4fe2-af56-927915427c1c","year":2017},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:39.921396Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:73a2740abdda56b6f23a273e8080c1f099fa5e39f9841bbf544cabc81bfdf78e","observation_id":"f9517f3e-d9fd-4cfd-80ae-ed7538960668","resolution":{"observed_at":"2026-08-12T13:37:40.979529Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.958781Z","title":"Surface defect saliency of magnetic tile","venue":null,"work_id":"1d9b59f9-1565-426e-bcfb-5cd1f3bb253c","year":null},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:39.926568Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:c71f018289fe8877dbd1763202849e74767a30a00533a793cb4354e883cd1f2c","observation_id":"02f0b6a1-78e1-4bc7-9c51-1b5ac8b82939","resolution":{"observed_at":"2026-08-12T13:37:40.963754Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.942616Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data, 2025","venue":null,"work_id":"886401cc-309b-4834-838c-3dee988cf94f","year":2025},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:39.931756Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:125931d7002599d4fa81d6bbe346737c633425e3c24ce89784f403897cca5c39","observation_id":"2a6d02d0-5f37-45e6-affa-a6f9ed7d7b59","resolution":{"observed_at":"2026-08-12T13:37:40.947636Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.926656Z","title":"Supplemen- tary document for fun-ad: Fully unsupervised learning for anomaly detection with noisy training data, 2025","venue":null,"work_id":"104e4b1b-17aa-4cf7-99b3-932a048c7b1b","year":2025},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:39.936296Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:63ec97310f8697964ed8c05cac1911cb54921bdf747e0359050b30e71e401b11","observation_id":"7cf26f80-c724-4335-a766-58768e66300a","resolution":{"observed_at":"2026-08-12T13:37:40.932095Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.910490Z","title":"Learning multiple layers of features from tiny images","venue":null,"work_id":"d149ffaa-ab2a-49dd-b394-26e7a7af7a88","year":2009},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:39.940913Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:cc05a002a30129ba8f55a8dad95c139c3a3335767a62658e53f3f22dea9b99ca","observation_id":"234ec5f0-fde0-4fd3-bee3-abcce4d445ab","resolution":{"observed_at":"2026-08-12T13:37:40.915631Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.895253Z","title":"Cutpaste: Self-supervised learning for anomaly de- tection and localization","venue":null,"work_id":"c32423c2-271e-48fd-8568-ee5af5ec8cc3","year":2021},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:39.945581Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:34787b2083e28ccc2ad9d1d62428a2754df1eb05f664eacc8a2c70f01dea2184","observation_id":"e313d6e0-163b-4ff9-a892-75c826adb6f1","resolution":{"observed_at":"2026-08-12T13:37:40.900243Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.878902Z","title":"Deep unsupervised anomaly detection","venue":null,"work_id":"76d68590-8c37-4212-997b-bbdeedeba3d6","year":2021},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:39.950290Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:7cf75bf7c084e8afde765990b4f47e6f4d52e085050b373e39869e91f2e256cf","observation_id":"c6f8a68d-53a2-48ad-961c-122ee775efcb","resolution":{"observed_at":"2026-08-12T13:37:40.884019Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.861400Z","title":"Simplenet: A simple network for image anomaly detection and localization","venue":null,"work_id":"ebaa179b-f562-41ca-83c0-65635dfbe68c","year":2023},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:39.955324Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:59884cfa2872f9f04db3774aded10b6f240e9820878e2d0acd479f526b6c213a","observation_id":"4b3823ac-9232-479a-a544-b87df53b1208","resolution":{"observed_at":"2026-08-12T13:37:40.866723Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.844976Z","title":"A compre- hensive survey on graph anomaly detection with deep learn- ing","venue":null,"work_id":"83133636-190f-4544-96f7-6591ca1c15e1","year":2021},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:39.960194Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:3ffabed82b788e2ea1ef3aaa52f2ed77f18ff3b0143fa07f4acc26a66fc018a2","observation_id":"f2c6c6df-7782-483c-aeb0-e6d932d8090f","resolution":{"observed_at":"2026-08-12T13:37:40.850714Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.829107Z","title":"One-Class SVMs for Document Classification","venue":null,"work_id":"f6b42bf7-e75d-4345-a646-d05a2b58d59d","year":2001},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:39.965065Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:da19557fcdb82ed558539b1dd6155825895346ef834e94b8029a06fbc6ec86eb","observation_id":"c84215f4-152a-4a7a-8c6f-fcaa79644950","resolution":{"observed_at":"2026-08-12T13:37:40.834043Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.813733Z","title":"Inter- realization channels: Unsupervised anomaly detection be- yond one-class classification","venue":null,"work_id":"91aab145-fec5-4078-8e03-9ac086183ed8","year":2023},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:39.969732Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:640b727718f7a0a38effe494a90c9ef6d346036530ade4f6a4d4f81ca0fc4d27","observation_id":"ffabaa4a-64e5-4127-ada2-5f0d648bec33","resolution":{"observed_at":"2026-08-12T13:37:40.818573Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.797192Z","title":"Self-trained deep ordinal regression for end-to-end video anomaly detection","venue":null,"work_id":"8219d7bc-3ea9-41b9-8127-cb44870f0e28","year":2020},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:39.974279Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:aab8e21c52191c4604bdd4c51de2d7bd67759eb33691a7fa7a54373d3c5d1ccd","observation_id":"c748ba40-23bf-42ed-84e9-e44c06510a35","resolution":{"observed_at":"2026-08-12T13:37:40.802627Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.781375Z","title":"Latent outlier exposure for anomaly detec- tion with contaminated data","venue":null,"work_id":"32d5fd5e-dafc-4ee3-a312-405141fe9e2c","year":2022},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:39.978840Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:faa7f567e0caa1ae5de152e3fcdaafaaa9333f5d4b917c36c75fffb3f7a2a4d5","observation_id":"2b264810-b0e2-4dd4-9582-80b2cfc4c1f2","resolution":{"observed_at":"2026-08-12T13:37:40.786289Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.766109Z","title":"Towards to- tal recall in industrial anomaly detection","venue":null,"work_id":"3bb7cd04-4635-4e1a-94f2-75292805b2e9","year":2022},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:39.983687Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:80ad91128c2b13cd0777c8d90800fb02dc59aa51b1d3557e063fc97238ba75df","observation_id":"077604a5-39cd-4472-b9b5-8a0d80de9423","resolution":{"observed_at":"2026-08-12T13:37:40.770833Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.748473Z","title":"Fully convolutional cross-scale-flows for image- based defect detection","venue":null,"work_id":"04d7a837-752e-4e5b-8846-9c23d3b65d17","year":2022},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:39.988306Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:49eb247aa2eefec40b58bb9725eafd87e8bf398b545ab9a32413527e3fdf86f1","observation_id":"2097efc1-72f7-42aa-84b8-cddd649b3870","resolution":{"observed_at":"2026-08-12T13:37:40.754811Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.733011Z","title":"Natural synthetic anomalies for self-supervised anomaly detection and localization","venue":null,"work_id":"cbecff82-29cf-4049-8f14-3b266cb8a4d1","year":2022},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:39.992873Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:f8e87a5385b6c2d61d5776cece30d2c0dfe861531a36d50ff93ca69860d73d6c","observation_id":"30192f3e-5afc-42a1-9003-3e109cb2e43f","resolution":{"observed_at":"2026-08-12T13:37:40.738250Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.716192Z","title":"Active Learning for Con- volutional Neural Networks: A Core-Set Approach","venue":null,"work_id":"e7f05566-3249-41f3-ba8c-821052d1f291","year":2018},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:39.997625Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:47734b40c8f1be3e155b1c456879bde0b51ed04433b34c4fa19536b48479a4ad","observation_id":"b2f7775d-0321-4867-a61c-727cac77b666","resolution":{"observed_at":"2026-08-12T13:37:40.721766Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.699768Z","title":"Anomaly detection using score-based per- turbation resilience","venue":null,"work_id":"e1f5fbf9-9778-4c87-8c86-a13875d860fe","year":2023},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:40.002525Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:da1ef4055b626622efa4485641a676f2492a18932fa597589f0264787cbbcfdd","observation_id":"5bfbfd52-f3fc-4a22-a85c-981a03c0ee2f","resolution":{"observed_at":"2026-08-12T13:37:40.705041Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.682898Z","title":"Revisiting reverse distillation for anomaly detection","venue":null,"work_id":"14ba2479-062c-4a1a-97f0-5b07efff42d1","year":2023},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:40.007339Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:2a3ad4fae4d805fefb7ea0cc1befd53821aa6bb806042a09b6c67288e27fd595","observation_id":"d24e0236-8979-465c-99e3-2f28d24a0280","resolution":{"observed_at":"2026-08-12T13:37:40.688099Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.665593Z","title":"Unsupervised feature learn- ing with c-svddnet","venue":null,"work_id":"9d06b080-435f-4887-8de8-fdc8fdb14639","year":2016},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:40.011871Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:39d77c09b1db3f3cdab6ad725faded1720a2a02628d948a169d73c2ffd3019b4","observation_id":"8fb6facb-22b3-4d38-99ac-05d3ac84a9d4","resolution":{"observed_at":"2026-08-12T13:37:40.671787Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.647223Z","title":"Hierarchical semi-supervised con- trastive learning for contamination-resistant anomaly detec- tion","venue":null,"work_id":"8260092e-af51-4f85-8182-b30a37d75144","year":2022},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:40.016663Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:640a0fff73e9f88a8e12e070b97d28a29946111071347a89a90e82cba5f61e34","observation_id":"95e08d66-2848-490e-86b3-18c5e00c8ff8","resolution":{"observed_at":"2026-08-12T13:37:40.653847Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.629945Z","title":"Glanc- ing at the patch: Anomaly localization with global and lo- cal feature comparison","venue":null,"work_id":"df98adb1-0541-4244-b319-0df4ef20726a","year":2021},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:40.021390Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:fe86b8c95ffacd9f12e167eeded463ba7e8f319a196587c54fc8f0d87985579b","observation_id":"5e206cdc-dc3a-4753-b2c6-5e89e933752f","resolution":{"observed_at":"2026-08-12T13:37:40.635840Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.611144Z","title":"Diffusion models for medical anomaly detection","venue":null,"work_id":"7189b235-7fea-45a8-a1d3-5381d20efb95","year":null},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:40.026066Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:766793768b2663f15ffcf9df9537336377d8b6435a7748c6c0262955eaee1c88","observation_id":"407b78b9-62d0-4b41-b26b-15c6ba1b0918","resolution":{"observed_at":"2026-08-12T13:37:40.617040Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.590984Z","title":"SoftPatch: Un- supervised anomaly detection with noisy data","venue":null,"work_id":"582e6bcc-c0ec-433d-96c6-40de1a3a70f4","year":null},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:40.030873Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:f9d4aaf95bbb5d4b6f0387168dff934f26130bd311b7fe007b2efc9d748f539e","observation_id":"7fc5ac83-8e70-4c6b-8f1c-9b60a4485cb6","resolution":{"observed_at":"2026-08-12T13:37:40.597343Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.557282Z","title":"Squid: Deep feature in-painting for unsupervised anomaly detec- tion","venue":null,"work_id":"72dec1d4-95cb-4377-b6dc-88b3309aff85","year":2023},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:40.041215Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:7a8fc166fd1546f02e6c72ef00d1c5ad6d1daea78cf32ef6a105aa1853aee410","observation_id":"c18f6cc8-a245-4840-9392-63116d26f335","resolution":{"observed_at":"2026-08-12T13:37:40.563009Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2301.13359","last_updated":"2024-01-28T02:20:41Z","snapshot_observed_at":"2026-08-15T20:14:32.301762Z","submitted_at":"2023-01-31T01:24:45Z","title":"IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.13359","snapshot_observed_at":"2026-08-12T13:37:40.046160Z","title":"Im-iad: Indus- trial image anomaly detection benchmark in manufacturing","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:40.046160Z"},"links":{"cited_paper":"/paper/2301.13359","citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:46e2408f4bf6f235548839010ab4e572c29f464fe0e5a82e1f48105c8636cfd2","observation_id":"1c1058be-48df-4807-bcf5-fe3d5533c15f","resolution":{"observed_at":"2026-08-12T13:37:40.046160Z","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":"2026-08-12T13:37:40.540078Z","title":"Explicit boundary guided semi-push-pull contrastive learning for supervised anomaly detection","venue":null,"work_id":"b2d6b98e-90d4-4783-9c1a-eb16bf21c493","year":null},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:40.051121Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:a3194e67b7ed4002a78b278c0cf4f0d219b05071f7410e065393b7d62ea4474c","observation_id":"c4ad1e8c-8025-4ff2-ad8b-8a2bd8162901","resolution":{"observed_at":"2026-08-12T13:37:40.545695Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.521523Z","title":"Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection","venue":null,"work_id":"fd020106-2074-4138-943e-f651ea2b96e5","year":2022},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:40.055746Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:24b0c401c918a615ee15deb0cbc83daecb047114870510e810fd644250458515","observation_id":"31bb2e97-1524-451f-8a3d-6fb3f6d1addf","resolution":{"observed_at":"2026-08-12T13:37:40.528743Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.504051Z","title":"Deep anomaly discovery from unla- beled videos via normality advantage and self-paced refine- ment","venue":null,"work_id":"071f918a-1309-4083-8095-8a367e8107af","year":2022},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:40.060543Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:650b0aa72e99402f929347622b293a7cb0d4c53f78dba1982d56ede7df913ecc","observation_id":"31763a71-f2f6-46d8-8024-2fa7cf301f22","resolution":{"observed_at":"2026-08-12T13:37:40.510505Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.486169Z","title":"Generative cooperative learning for unsupervised video anomaly detection","venue":null,"work_id":"54785918-a1d9-458d-9df7-13c628225aae","year":2022},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:40.065137Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:cfe393aeb91212374192879de3d6e10718fd228c5d3204152c9040b54c96daa7","observation_id":"5d1307a4-c0f8-4f48-88d6-2b97fef80bbc","resolution":{"observed_at":"2026-08-12T13:37:40.492756Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.467986Z","title":"Draem- a discriminatively trained reconstruction embedding for sur- face anomaly detection","venue":null,"work_id":"a1c1e51a-6a66-497c-a836-836a02433791","year":null},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:40.069803Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:af331a12cb685206360eb93dc72c6b91e5244364f7b7bebe4c6dd9f34e1c91a5","observation_id":"e1d755d4-4e9a-45f0-9b00-b63fd33d27c4","resolution":{"observed_at":"2026-08-12T13:37:40.474205Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.448061Z","title":"Dsr– a dual subspace re-projection network for surface anomaly detection","venue":null,"work_id":"485b9cf9-a7d5-4421-97ec-58a1fbeb090d","year":2022},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:40.074255Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:097c982497acc95581daea706c44f062c6c05a6f8e427f8179fae72b568ef334","observation_id":"24b61e89-2280-4aec-8a9d-3978cff9fdd8","resolution":{"observed_at":"2026-08-12T13:37:40.455650Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.431454Z","title":"Prototypical residual networks for anomaly detection and localization","venue":null,"work_id":"299a53b3-9519-4761-8311-3465c9fa00f4","year":2023},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:40.079047Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:a451beb1a5915080c7e22336917345f139629d9143962a4fb3648276c5ffc271","observation_id":"94d2d993-2aa2-4073-8d97-a79f546ba34c","resolution":{"observed_at":"2026-08-12T13:37:40.437028Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.412099Z","title":"Destseg: Segmentation guided denoising student-teacher for anomaly detection","venue":null,"work_id":"215aa802-e39b-4998-ad4c-d05c692164d5","year":2023},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:40.083969Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:566cb839dae24770d30c184ec60330f46158c7ae048514eb2ee16720bf8e7f93","observation_id":"a12eeb19-a6d0-4ffc-8b20-53997c43d44e","resolution":{"observed_at":"2026-08-12T13:37:40.418175Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.05897","last_updated":"2024-03-09T12:25:01Z","snapshot_observed_at":"2026-08-13T00:58:46.505557Z","submitted_at":"2024-03-09T12:25:01Z","title":"RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.05897","snapshot_observed_at":"2026-08-12T13:37:40.089004Z","title":"Real- Net: A Feature Selection Network with Realistic Syn- thetic Anomaly for Anomaly Detection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:40.089004Z"},"links":{"cited_paper":"/paper/2403.05897","citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:32271a6fb3794f2776a29c151640f36ea6a68c8bdd4580fb1d39e588c44c0941","observation_id":"0e2f3c1f-36a1-4f47-95ac-eb4b6e514672","resolution":{"observed_at":"2026-08-12T13:37:40.089004Z","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":"2026-08-12T13:37:40.391488Z","title":"Learning with local and global consistency.Advances in Neural Information Process- ing Systems, 16, 2003","venue":null,"work_id":"c1720064-9c9c-4506-8a09-cc49ad6db1ef","year":2003},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:40.094077Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:0c8c060c70fb1d61999e3392a4c1f0c51b17ebd3980ec8ccaafe308d220e215f","observation_id":"8e56ac49-c6af-441f-9a75-1356de7506a1","resolution":{"observed_at":"2026-08-12T13:37:40.398195Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.373639Z","title":"Spot-the-difference self-supervised pre- training for anomaly detection and segmentation","venue":null,"work_id":"b1541d26-41c9-498b-af61-33f95b042c13","year":2022},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:40.099253Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:f4e10ba2f30178a14f26b22b21f82053ffe9b8dadd7d1e0bf6f1868018cfdd2b","observation_id":"af407856-f001-4922-9cec-9544e13fe9b2","resolution":{"observed_at":"2026-08-12T13:37:40.379751Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.356596Z","title":"Therefore, we aim to bridge this theoretical gap with empirical analy- sis using real-world data","venue":null,"work_id":"0dd7776b-0a25-4a8b-b3bd-987ac76ba92c","year":null},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:40.104037Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:da001da9d2286501e3cdb72ab5ba546756fdbb80c98c6ca4c988d1b4ff3406f9","observation_id":"5be99491-ce09-4215-b384-8cbd1dfd8d16","resolution":{"observed_at":"2026-08-12T13:37:40.361991Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.338875Z","title":"au- tomobile","venue":null,"work_id":"4d4f369b-2c3d-4bd4-9d27-8c619c2ecfbb","year":null},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:40.108793Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:06440d3603523367750c6275fad0d1387dc23689989f565148f63241374f230c","observation_id":"8f337b73-855e-49cd-8b7c-4a212c5e666c","resolution":{"observed_at":"2026-08-12T13:37:40.344721Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.320420Z","title":"E takes an image Ii as input and outputs one class token and P patch tokens","venue":null,"work_id":"56920c74-0a3f-4a87-b305-93e3a4191660","year":null},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:40.113599Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:00ecbadd1bfad09a0474227a6f9b506f7effaae10f99ebc536b63a67f7532f78","observation_id":"61e42b86-d332-4659-bb68-92ca3448ca62","resolution":{"observed_at":"2026-08-12T13:37:40.326103Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.303228Z","title":"1 demon- strates the performance of FUN-AD according to the con- tamination ratio in the training dataset","venue":null,"work_id":"0326c5f0-590d-4454-b7af-3ba641898ce2","year":null},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:40.120365Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:a3dedd6d69ad621b0bf1e023d9c2aba6aad74c71d4b5f0d201f1c6688ceb79b4","observation_id":"5e933c59-10e1-42cc-b56f-b919ae9d1eba","resolution":{"observed_at":"2026-08-12T13:37:40.309015Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.286826Z","title":"2 shows some anomaly localization results yielded by FUN-AD","venue":null,"work_id":"cbcad87e-042e-47fc-9d9b-834aabfd8b3c","year":null},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:40.126021Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:599f0655b6892976368f34f6de0bd550d0ab6d9d16c223aca0d773f40a17c51f","observation_id":"1ba17e3e-cb58-413b-a2a7-ee2b26bea345","resolution":{"observed_at":"2026-08-12T13:37:40.292462Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.269609Z","title":"7, 8, 9, 10","venue":null,"work_id":"d1146d60-7022-46d7-9df5-30c0fa7bfaed","year":null},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:40.130780Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:9c441482cf0dd7e9785fa1009b7b8d3ec514a6ed24120b5ba9d6452ae8920184","observation_id":"64668209-1031-4a96-9f16-b8e095fc63c0","resolution":{"observed_at":"2026-08-12T13:37:40.274981Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.248423Z","title":"when one type of anomaly dominates","venue":null,"work_id":"c3792a31-f26f-4fdf-8e7c-96ba32045fe8","year":null},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:40.135365Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:ae8d2d413de99e793627259553b75d3ee6399fe2dfbfdbae081f340c7c4022b5","observation_id":"5f40e8c2-2f96-46c0-b785-2f03cfd4f992","resolution":{"observed_at":"2026-08-12T13:37:40.257836Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:37:40.574213Z","title":null,"venue":null,"work_id":"5091b95b-3c41-4a1e-9a56-da1920ecb8c1","year":null},"citing_paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:40.035837Z"},"links":{"citing_paper":"/paper/2411.16110"},"observation_digest":"sha256:b62b6acea2873aaffb007d779bdf830239fb4d9e2e0d764d923bff6ae5e67516","observation_id":"4dc1c526-5ec2-40ca-b0d8-8f7ac5b4dbd6","resolution":{"observed_at":"2026-08-12T13:37:40.579831Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2411.16110","last_updated":"2024-11-25T05:51:38Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-15T11:19:04.638253Z","submitted_at":"2024-11-25T05:51:38Z","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data"},"reference_resolution":{"displayed":59,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":0,"verified_fuzzy":52},"total_outbound_references":59},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 1 inbound Pith citation observation for arXiv:2411.16110."}