{"as_of":"2026-08-08T01:54:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7807fea492d2d9e9ffefd8dd4fd0cf694b95fa1699079aa105fb418107f3741f","coverage":[{"denominator":35,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":35,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T05:05:37.673869Z","state":"measured"},{"denominator":35,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":35,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.08784/citation-record","integrity":"/paper/2506.08784/integrity","json":"/paper/2506.08784/citation-record.json","paper":"/paper/2506.08784"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:05:39.859960Z","title":"Ganomaly: Semi- supervised anomaly detection via adversarial training","venue":null,"work_id":"04051bd9-1ad9-42a3-9555-5f94bcd22d41","year":2018},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:34.398137Z"},"links":{"citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:26487bdd4b6b1607d281ee3eb861bff95bd61795b1760f4e7a5f092b87d3f15d","observation_id":"0642ec71-0809-4638-af8a-c78c570e969b","resolution":{"observed_at":"2026-08-07T05:05:39.938579Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T05:05:39.699187Z","title":"Image based quality inspection in smart manufacturing systems: A literature review.Procedia CIRP, 103:262–267, 2021","venue":null,"work_id":"d42d50b5-e73a-415b-b57e-a380e8084b55","year":2021},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:34.487385Z"},"links":{"citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:8fa970bab50aeea0a9b148a13a188aea5ecf7dc4002f365b2ad532f291043624","observation_id":"ff9868b1-05b9-42cb-abcc-4ddb33484413","resolution":{"observed_at":"2026-08-07T05:05:39.775909Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T05:05:39.674066Z","title":"Lucas-kanade 20 years on: A unifying frame- work.International journal of computer vision, 56(3):221–255, 2004","venue":null,"work_id":"91b7442d-f869-49ce-8160-76b17f806a9a","year":2004},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:34.583109Z"},"links":{"citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:a04c0cd80b8d1afc532d3a774ed5b13f4f8186bc1bf7c8d9c40b845bbc7c1df3","observation_id":"5eab584f-9b83-419f-8dc3-91d6692b8a15","resolution":{"observed_at":"2026-08-07T05:05:39.679935Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.10445","last_updated":"2020-02-24T18:51:33Z","snapshot_observed_at":"2026-07-06T08:59:42.044090Z","submitted_at":"2020-02-24T18:51:33Z","title":"Deep Nearest Neighbor Anomaly Detection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.10445","snapshot_observed_at":"2026-08-07T05:05:34.668810Z","title":"Deep nearest neighbor anomaly detection.arXiv preprint arXiv:2002.10445, 2020","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:34.668810Z"},"links":{"cited_paper":"/paper/2002.10445","citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:3d83d721447effbd7f665138c94f691ccdb3ba97b0bd8ac30367556222c9496c","observation_id":"b847da18-8e1f-44cb-8c07-6e97f3efbef9","resolution":{"observed_at":"2026-08-07T05:05:34.668810Z","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":"2026-08-07T05:05:39.661579Z","title":"Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection","venue":null,"work_id":"b8cd4e4b-bfd8-47ec-a3b8-6f9dda17e3f1","year":2019},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:34.756762Z"},"links":{"citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:610fe682dc9508c7be226960ad64389d6b9545b716f3541fcc9e5a30b51961a6","observation_id":"ad490edc-9e1f-4ec8-8939-6a3af101b36c","resolution":{"observed_at":"2026-08-07T05:05:39.668283Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T05:05:39.642070Z","title":"Clkn: Cascaded lucas- kanade networks for image alignment","venue":null,"work_id":"b78e86ed-20c8-4280-9c82-6c4f94bb04d0","year":2017},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:34.902293Z"},"links":{"citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:8ded238a6fd0d7b7f38d28dd69c22dd87f85576463fafdbc30cf0e5b36f5bad0","observation_id":"a292634a-4737-4c56-a329-a5860b757881","resolution":{"observed_at":"2026-08-07T05:05:39.648054Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.02357","last_updated":"2021-02-03T16:28:51Z","snapshot_observed_at":"2026-07-06T09:17:53.298286Z","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":"2026-08-07T05:05:35.053876Z","title":"Sub-image anomaly detection with deep pyramid correspondences.arXiv preprint arXiv:2005.02357, 2020","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:35.053876Z"},"links":{"cited_paper":"/paper/2005.02357","citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:8fb00eadb66990e45819972d78b473b950be7866828287645f66f5ea28072ec9","observation_id":"935eaeda-def4-4407-b4b1-553d8cdc7f23","resolution":{"observed_at":"2026-08-07T05:05:35.053876Z","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":"2026-08-07T05:05:39.633006Z","title":"Padim: a patch distribution modeling framework for anomaly detection and lo- calization","venue":null,"work_id":"51ba93fb-d8f3-4cde-8310-e0a4c9b91117","year":2021},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:35.210582Z"},"links":{"citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:cacb80940377485db80cfcad64219868108c923c94a627cb9ddc83cb0479a03b","observation_id":"cda040f0-4d01-4d6e-ad6e-0e8670716b8b","resolution":{"observed_at":"2026-08-07T05:05:39.636325Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T05:05:35.330728Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:35.330728Z"},"links":{"citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:49dcc5cee9a3586c9bd4b4317794d8dd4e1fb6f62f996ce6cc310b1b7ec50521","observation_id":"fa210436-4a52-491b-b304-9bed30fe2855","resolution":{"observed_at":"2026-08-07T05:05:35.330728Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1606.03798","last_updated":"2016-06-13T02:46:38Z","snapshot_observed_at":"2026-07-06T04:59:41.489500Z","submitted_at":"2016-06-13T02:46:38Z","title":"Deep Image Homography Estimation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.03798","snapshot_observed_at":"2026-08-07T05:05:35.444759Z","title":"Deep image ho- mography estimation.arXiv preprint arXiv:1606.03798, 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:35.444759Z"},"links":{"cited_paper":"/paper/1606.03798","citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:45bd03a342b78cd8f369b2ca5cffaddc78ccc6e72ed47b076639c8a8efa3fb7a","observation_id":"bd21d5a0-e41d-4c94-b69f-67ff2bca894d","resolution":{"observed_at":"2026-08-07T05:05:35.444759Z","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":"2026-08-07T05:05:39.618507Z","title":"Homography estimation from image pairs with hierarchical convolutional networks","venue":null,"work_id":"a069046d-4212-490d-bcab-375346da94e2","year":2017},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:35.624675Z"},"links":{"citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:15146084fd1b50e2effe44e25423975e22af244ecadc3c49c60746824e02aadb","observation_id":"514c0ae5-0899-4e6d-ac93-053975dce884","resolution":{"observed_at":"2026-08-07T05:05:39.621382Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.12231","last_updated":"2022-11-09T23:15:15Z","snapshot_observed_at":"2026-08-02T03:51:09.933624Z","submitted_at":"2018-11-29T15:04:05Z","title":"ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.12231","snapshot_observed_at":"2026-08-07T05:05:35.769910Z","title":"Imagenet-trained cnns are biased towards tex- ture; increasing shape bias improves accuracy and robustness.arXiv preprint arXiv:1811.12231, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:35.769910Z"},"links":{"cited_paper":"/paper/1811.12231","citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:2d62702f71230af27069ac7401beb88efee9e13972ad47f8d758bee79612a6b8","observation_id":"544c26a0-c0ea-495f-977e-7f2b516cd8d2","resolution":{"observed_at":"2026-08-07T05:05:35.769910Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1803.07728","last_updated":"2018-03-21T03:21:14Z","snapshot_observed_at":"2026-07-06T06:29:22.866136Z","submitted_at":"2018-03-21T03:21:14Z","title":"Unsupervised Representation Learning by Predicting Image Rotations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.07728","snapshot_observed_at":"2026-08-07T05:05:35.918752Z","title":"Unsupervised represen- tation learning by predicting image rotations.arXiv preprint arXiv:1803.07728, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:35.918752Z"},"links":{"cited_paper":"/paper/1803.07728","citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:002119980ca078d39e8ac9da56580c1adc54a6c468383fd69e7a41d9a9d835fc","observation_id":"66b67abd-841e-46d6-a4fc-f5754ad180e2","resolution":{"observed_at":"2026-08-07T05:05:35.918752Z","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":"2026-08-07T05:05:39.609729Z","title":"Deep anomaly detection using geometric trans- formations.Advances in neural information processing systems, 31, 2018","venue":null,"work_id":"03524e21-9e82-49ab-a1fa-96cce1837e56","year":2018},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:36.011798Z"},"links":{"citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:3f329040c90ae028b2d53f386f87d0995b729223c4a385b08ef5b05ead2d0d05","observation_id":"ca8d7229-975d-46f5-91a8-f68bba0a8cae","resolution":{"observed_at":"2026-08-07T05:05:39.613119Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T05:05:39.600777Z","title":"Generative adver- sarial nets.Advances in neural information processing systems, 27, 2014","venue":null,"work_id":"050129f8-925c-4fcd-bb17-d4e6e55b7a50","year":2014},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:36.094515Z"},"links":{"citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:231abf220fe9866eda21ee820cc252da325d85a9986f1c3e5011b7d69e6d3dd9","observation_id":"84ce5357-5da4-4757-929d-fd305c05ce9d","resolution":{"observed_at":"2026-08-07T05:05:39.604341Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T05:05:39.592333Z","title":"Deep residual learn- ing for image recognition","venue":null,"work_id":"da182fc9-a3f6-46a2-9f28-fb6e3e19c8a8","year":2016},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:36.154369Z"},"links":{"citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:40b8ff37818e795db64eee334be6e0a8de8cdc574af196ad85aea1302c9d71ef","observation_id":"b51f5d8f-6907-47f0-96bc-4560484c142c","resolution":{"observed_at":"2026-08-07T05:05:39.595167Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T05:05:39.582623Z","title":"Using self- supervised learning can improve model robustness and uncertainty.Advances in neural information processing systems, 32, 2019","venue":null,"work_id":"7e57b45e-148a-44f3-b18b-1609aea01c9a","year":2019},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:36.243351Z"},"links":{"citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:53432a443ce0f1237c91a026a147aa857ea057aec428cb5492a5cc4336fd26aa","observation_id":"3d873e35-0ce4-4500-8f91-76852d458801","resolution":{"observed_at":"2026-08-07T05:05:39.585960Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T05:05:39.571000Z","title":"Surface defect saliency of magnetic tile.The Visual Computer, 36(1):85–96, 2020","venue":null,"work_id":"3abc1587-d830-41f9-b7de-a805986da9b5","year":2020},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:36.323662Z"},"links":{"citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:8ebcbd8e7897b11d558c29938ef21d606e2986534fee748666a0b6ce34fb040f","observation_id":"1c8660eb-8350-425f-a0f2-0f9d831fc27e","resolution":{"observed_at":"2026-08-07T05:05:39.574065Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T05:05:39.562539Z","title":"Perceptual loss for robust unsupervised homography estimation","venue":null,"work_id":"c4f0c0dc-6412-48ee-a520-2b7424609459","year":2021},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:36.412818Z"},"links":{"citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:be5cc6f8f1826ee58ce3933aaa5fa0283be447766123b2e2133bc93294d9a3bf","observation_id":"a3e4f0dc-c5cb-4d44-b334-93a9803e1c73","resolution":{"observed_at":"2026-08-07T05:05:39.565551Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T05:05:39.553557Z","title":"Distinctive image features from scale-invariant keypoints.Inter- national journal of computer vision, 60(2):91–110, 2004","venue":null,"work_id":"b5d3c8f1-762a-4fd9-8d20-70e5ddd8b885","year":2004},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:36.536541Z"},"links":{"citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:356ce104fba8e4f6d79edbdbcd5bc401ec4479da12f3d7e507d76f291ed3ea9c","observation_id":"bdc52ba0-e389-4b57-9962-c9a92c591de6","resolution":{"observed_at":"2026-08-07T05:05:39.556798Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T05:05:39.544606Z","title":"On the generalized distance in statistics","venue":null,"work_id":"37eddeba-ce97-43be-b7a7-b18a377bacbb","year":1936},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:36.626605Z"},"links":{"citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:ee593423cec39fb5bf7f3fe265df7d23af42459f34f0d0f74b4565079b452110","observation_id":"dec2dbba-fb18-40e8-9505-f609ab060d61","resolution":{"observed_at":"2026-08-07T05:05:39.547535Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T05:05:39.535635Z","title":"Vt-adl: A vision transformer network for image anomaly detection and localization","venue":null,"work_id":"6ae3d742-7df7-4f63-9eb8-20f370a53cc5","year":2021},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:36.742915Z"},"links":{"citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:fe52985999921a9e12442355a26ebce72ee22a08c051111c9e575ba0dec0762a","observation_id":"aa1f35a9-df35-4ba4-b0a1-ad867c00c930","resolution":{"observed_at":"2026-08-07T05:05:39.538963Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T05:05:39.525414Z","title":"Unsupervised deep homography: A fast and robust homography estimation model.IEEE Robotics and Automation Letters, 3(3):2346–2353, 2018","venue":null,"work_id":"b8687bcd-2cb8-4762-8b79-aca2c5e15d01","year":2018},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:36.832597Z"},"links":{"citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:c47b39fc92439fd6367950cc9eb0a1ae62407beb5f05673c0eb87c4491625f54","observation_id":"73165335-fe01-45d3-be05-34e45fce0bdf","resolution":{"observed_at":"2026-08-07T05:05:39.529409Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T05:05:39.395819Z","title":null,"venue":null,"work_id":"892aa5e0-d1fc-40fe-8162-f57829e275c2","year":2003},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:36.901566Z"},"links":{"citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:750ba2ab3e0338b38da109f085fecb38cd76c7b2f2cbf005f2481c6ba22970a4","observation_id":"0b60211d-42c5-401c-9b57-21a7dda5337c","resolution":{"observed_at":"2026-08-07T05:05:39.518627Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T05:05:39.119203Z","title":"Deep learning for anomaly detection: A review.ACM Computing Surveys (CSUR), 54(2):1–38, 2021","venue":null,"work_id":"38e2ebb8-c078-40e2-9d4e-c037494b9e9c","year":2021},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:36.974498Z"},"links":{"citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:59868ed3079b6104184b5e90324fe756f9da6ee302fca6539db2b96c1a21dd3f","observation_id":"26af7b95-f172-4364-85ee-195a71c8461e","resolution":{"observed_at":"2026-08-07T05:05:39.263630Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T05:05:38.789190Z","title":"Modeling the distribution of normal data in pre-trained deep features for anomaly detection","venue":null,"work_id":"6abae940-8a3b-4362-9a79-ed7d97123834","year":2021},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:37.063169Z"},"links":{"citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:705588d400284f4979f5cda15e27182b3b548ecf3fb91ed14378a031ebf442ad","observation_id":"b0979a5a-4ef5-4e5e-b25e-c6744b34d81a","resolution":{"observed_at":"2026-08-07T05:05:38.969238Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T05:05:38.702243Z","title":"Towards total recall in industrial anomaly detection","venue":null,"work_id":"076712f3-5fb3-4677-9e49-6e308881f3f3","year":2022},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:37.152102Z"},"links":{"citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:8a508bb36827f5c033be75b49806c5e2b10adb608aa31cab03034cfdd2589476","observation_id":"6944a0cf-8239-49af-9cab-99b24adf9736","resolution":{"observed_at":"2026-08-07T05:05:38.744462Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T05:05:38.552135Z","title":"Orb: An ef- ficient alternative to sift or surf","venue":null,"work_id":"e33dfb0a-dd88-4872-97b3-f3a0cedb3330","year":2011},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:37.242311Z"},"links":{"citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:d6d3f02ff9d5a1115bbf2f0be427673bc4650a1c0a372824ac74a326926128aa","observation_id":"36a98c57-7273-48cf-89ea-3d4ef3e9c5c0","resolution":{"observed_at":"2026-08-07T05:05:38.620183Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T05:05:38.414615Z","title":"Deep one-class classification","venue":null,"work_id":"6addfcac-9b0b-4283-9c2a-df5e30685949","year":null},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:37.333246Z"},"links":{"citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:29e2a3997ee796295945ea3cd0ed5d93568a86f4fa5bbf33cf63f7834e3f2c6a","observation_id":"c25fcaae-a099-4093-b5cd-491ed2b82394","resolution":{"observed_at":"2026-08-07T05:05:38.478911Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T05:05:38.292382Z","title":"Unsupervised anomaly detection with generative ad- versarial networks to guide marker discovery","venue":null,"work_id":"043b6793-9680-4bef-a138-823d288b8874","year":2017},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:37.420126Z"},"links":{"citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:3ead76c357e291f52f85d6fd9e3b2d10ae27db9cf6cbb50539046aa833c5d78b","observation_id":"58f6ec61-22b6-4b64-9380-6f82b19b50fc","resolution":{"observed_at":"2026-08-07T05:05:38.347949Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2011.02578","last_updated":"2021-03-25T23:11:23Z","snapshot_observed_at":"2026-07-06T10:11:48.783370Z","submitted_at":"2020-11-04T23:33:41Z","title":"Learning and Evaluating Representations for Deep One-class Classification","version":2},"cited_work":{"arxiv_id":"2011.02578","doi":null,"metadata_source":"pith","pith_arxiv_id":"2011.02578","snapshot_observed_at":"2026-08-07T05:05:37.763626Z","title":"Learning and Evaluating Representations for Deep One-class Classification","venue":"cs.CV","work_id":"b4617300-56db-4c54-8f3f-0633fc1b9091","year":2020},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:37.471083Z"},"links":{"cited_paper":"/paper/2011.02578","citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:2586b5afac03b4c96fe53c8ecf8da8e9166a79424744c461509413a02c7b263e","observation_id":"1fb262a5-de9b-4fe9-a242-a12c63719b9a","resolution":{"observed_at":"2026-08-07T05:05:37.794037Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T05:05:38.156014Z","title":"Efficientnet: Rethinking model scaling for convolu- tional neural networks","venue":null,"work_id":"e47f40f8-d891-4f5d-92aa-5171a54995af","year":2019},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:37.521838Z"},"links":{"citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:e3dbdc6a73911e3cad4277e8f1c943d817f27a15e6491b60c0b4385f8c411587","observation_id":"a4f494a5-2fa5-41d8-bda6-415f3cdedad3","resolution":{"observed_at":"2026-08-07T05:05:38.222523Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1605.07146","last_updated":"2017-06-14T06:06:48Z","snapshot_observed_at":"2026-08-01T16:47:50.594148Z","submitted_at":"2016-05-23T19:27:13Z","title":"Wide Residual Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1605.07146","snapshot_observed_at":"2026-08-07T05:05:37.574394Z","title":"Wide residual networks.arXiv preprint arXiv:1605.07146, 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:37.574394Z"},"links":{"cited_paper":"/paper/1605.07146","citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:decdd58546e2790627988ca7d2b26b2d542751c7aca80d65f28f349bd894f58a","observation_id":"162ec43b-49bc-4ecb-8405-9c03b8dbaae7","resolution":{"observed_at":"2026-08-07T05:05:37.574394Z","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":"2026-08-07T05:05:38.025351Z","title":"Rethinking planar homography estimation using perspective fields","venue":null,"work_id":"78412fd0-380a-4974-b84b-9dda91e8df45","year":2018},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:37.622409Z"},"links":{"citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:b30b23f0f8fc87846e51cacf5c473b255a1e0a85f5e30f66c83d8dea2c742ae2","observation_id":"879439a1-0c11-4ca4-af8d-2eba50ee5aed","resolution":{"observed_at":"2026-08-07T05:05:38.103320Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T05:05:37.907551Z","title":"Content-aware unsupervised deep homography estimation","venue":null,"work_id":"98886ddc-dcf2-4a50-9036-ed26ca2bc787","year":2020},"citing_paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:37.673869Z"},"links":{"citing_paper":"/paper/2506.08784"},"observation_digest":"sha256:94c953289565b18d25a392d58d1c3c765e2ce3a8a61a0d87eedee62f87089d47","observation_id":"fd328788-9520-40d6-b1a5-0d93df1268f9","resolution":{"observed_at":"2026-08-07T05:05:37.967695Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.08784","last_updated":"2025-06-10T13:32:20Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T04:59:42.404185Z","submitted_at":"2025-06-10T13:32:20Z","title":"HomographyAD: Deep Anomaly Detection Using Self Homography Learning"},"reference_resolution":{"displayed":35,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":8,"verified_exact":1,"verified_fuzzy":26},"total_outbound_references":35},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2506.08784."}