{"as_of":"2026-08-18T22:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:57bb6f887c9790f953432da9ebdbf984cd288cf6fc192963bcd164460acf23e2","coverage":[{"denominator":28,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":28,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T05:12:58.557196Z","state":"measured"},{"denominator":29,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":29,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+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":"2026-05-10T20:16:06.916465Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-10T22:05:48.339193Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2504.21294","last_updated":"2025-04-30T03:59:58Z","snapshot_observed_at":"2026-08-17T13:31:06.395780Z","submitted_at":"2025-04-30T03:59:58Z","title":"Learning Multi-view Multi-class Anomaly Detection","version":1},"cited_work":{"arxiv_id":"2504.21294","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.21294","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"e0779394-8fb4-4bec-bc71-68a22de4bb02","year":2025},"citing_paper":{"arxiv_id":"2604.05632","last_updated":"2026-04-07T09:35:29Z","snapshot_observed_at":"2026-08-15T04:26:20.796353Z","submitted_at":"2026-04-07T09:35:29Z","title":"SGANet: Semantic and Geometric Alignment for Multimodal Multi-view Anomaly Detection","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-10T20:16:06.916465Z"},"links":{"cited_paper":"/paper/2504.21294","citing_paper":"/paper/2604.05632"},"observation_digest":"sha256:8fa1f3d16817e5bcb4d510da14028bde857d211c7845c1d4bdf2e313b2f11858","observation_id":"7df5aec7-9e47-407b-b871-f96db9aa81b1","resolution":{"observed_at":"2026-05-10T22:05:48.341230Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2504.21294/citation-record","integrity":"/paper/2504.21294/integrity","json":"/paper/2504.21294/citation-record.json","paper":"/paper/2504.21294"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T05:12:59.312789Z","title":"Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection","venue":null,"work_id":"4ecc6bf6-aecc-47cd-af46-6e5f7e646806","year":2019},"citing_paper":{"arxiv_id":"2504.21294","last_updated":"2025-04-30T03:59:58Z","snapshot_observed_at":"2026-08-17T13:31:06.395780Z","submitted_at":"2025-04-30T03:59:58Z","title":"Learning Multi-view Multi-class Anomaly Detection","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T05:12:58.323350Z"},"links":{"citing_paper":"/paper/2504.21294"},"observation_digest":"sha256:acb9eba023a265ede30e61ec7e8bc4a9079d58396a59db11cf68f45021c069bc","observation_id":"8ed3e439-9516-47f5-8666-a8b4235767bb","resolution":{"observed_at":"2026-08-16T05:12:59.319833Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:12:59.295082Z","title":"Uninformed students: Student-teacher anomaly detection with discrimi- native latent embeddings","venue":null,"work_id":"16e4b1c6-726e-4014-8319-a4ee5c984d87","year":2020},"citing_paper":{"arxiv_id":"2504.21294","last_updated":"2025-04-30T03:59:58Z","snapshot_observed_at":"2026-08-17T13:31:06.395780Z","submitted_at":"2025-04-30T03:59:58Z","title":"Learning Multi-view Multi-class Anomaly Detection","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-16T05:12:58.329358Z"},"links":{"citing_paper":"/paper/2504.21294"},"observation_digest":"sha256:00bcfea8c7a515b9667beeb232dccc966740e5fe5690609a51412311d0e75b2c","observation_id":"268fc390-2e06-4223-9af5-4ffc3d64ce55","resolution":{"observed_at":"2026-08-16T05:12:59.299373Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:12:59.277766Z","title":"Multi-view 3d object detection network for autonomous driving","venue":null,"work_id":"52e936fa-e822-4fe7-8f34-54da54fbda4b","year":1907},"citing_paper":{"arxiv_id":"2504.21294","last_updated":"2025-04-30T03:59:58Z","snapshot_observed_at":"2026-08-17T13:31:06.395780Z","submitted_at":"2025-04-30T03:59:58Z","title":"Learning Multi-view Multi-class Anomaly Detection","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-16T05:12:58.337876Z"},"links":{"citing_paper":"/paper/2504.21294"},"observation_digest":"sha256:8b2f589b538fe36b156d7e39f44d8f3cd2feae4224e3631d4ffc941f3694d1d0","observation_id":"07155f58-db55-4ee1-aac7-9a6d4a0f5582","resolution":{"observed_at":"2026-08-16T05:12:59.283474Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.16588","last_updated":"2024-04-12T09:38:33Z","snapshot_observed_at":"2026-08-07T09:40:32.614733Z","submitted_at":"2023-09-28T16:45:46Z","title":"Vision Transformers Need Registers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.16588","snapshot_observed_at":"2026-08-16T05:12:58.343099Z","title":"Vision transformers need registers","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.21294","last_updated":"2025-04-30T03:59:58Z","snapshot_observed_at":"2026-08-17T13:31:06.395780Z","submitted_at":"2025-04-30T03:59:58Z","title":"Learning Multi-view Multi-class Anomaly Detection","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-16T05:12:58.343099Z"},"links":{"cited_paper":"/paper/2309.16588","citing_paper":"/paper/2504.21294"},"observation_digest":"sha256:8cc266b89013c3cdc5abbe211c8515fdeb5d54f5d4a2add78ae185cd3e4c391d","observation_id":"8619ff01-792e-4f4d-a4fc-0704a3da401e","resolution":{"observed_at":"2026-08-16T05:12:58.343099Z","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":"2026-08-16T05:12:59.259166Z","title":"Anomaly detection via reverse distillation from one-class embedding","venue":null,"work_id":"b64cb577-e187-45b3-b166-579955743156","year":2022},"citing_paper":{"arxiv_id":"2504.21294","last_updated":"2025-04-30T03:59:58Z","snapshot_observed_at":"2026-08-17T13:31:06.395780Z","submitted_at":"2025-04-30T03:59:58Z","title":"Learning Multi-view Multi-class Anomaly Detection","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-16T05:12:58.352896Z"},"links":{"citing_paper":"/paper/2504.21294"},"observation_digest":"sha256:2cccc0e1b10922a7edebad971b7bc72511182e21599c414494b7131896b2b9dc","observation_id":"49a308bf-1f8a-4ed3-bc33-f3effea52cd4","resolution":{"observed_at":"2026-08-16T05:12:59.263748Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:12:59.232714Z","title":"Prioritized local matching network for cross-category few-shot anomaly detection","venue":null,"work_id":"dabd059a-3ac6-45aa-95df-680a56880063","year":2024},"citing_paper":{"arxiv_id":"2504.21294","last_updated":"2025-04-30T03:59:58Z","snapshot_observed_at":"2026-08-17T13:31:06.395780Z","submitted_at":"2025-04-30T03:59:58Z","title":"Learning Multi-view Multi-class Anomaly Detection","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-16T05:12:58.359212Z"},"links":{"citing_paper":"/paper/2504.21294"},"observation_digest":"sha256:68ac006ecdd1ab8beeacefa4dd9ba4c3b826a396602352c633fe1388ec9b7aac","observation_id":"c9ce3c7b-5092-4562-ac37-f9ea7ac82ccc","resolution":{"observed_at":"2026-08-16T05:12:59.241394Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:12:59.201970Z","title":"Nng-mix: Improving semi-supervised anomaly detection with pseudo- anomaly generation","venue":null,"work_id":"d525a028-5fda-41ff-b4c7-f266fa5e7b19","year":2024},"citing_paper":{"arxiv_id":"2504.21294","last_updated":"2025-04-30T03:59:58Z","snapshot_observed_at":"2026-08-17T13:31:06.395780Z","submitted_at":"2025-04-30T03:59:58Z","title":"Learning Multi-view Multi-class Anomaly Detection","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-16T05:12:58.370615Z"},"links":{"citing_paper":"/paper/2504.21294"},"observation_digest":"sha256:badefc83eb84f42eacfa03e0d7287308ccdff993b3908f747dbcba993c659257","observation_id":"f0efe6f3-56ae-4685-b9df-0a3ef58fc515","resolution":{"observed_at":"2026-08-16T05:12:59.219093Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:12:59.183492Z","title":"Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection","venue":null,"work_id":"d817958d-76a5-45b7-91c7-27370d46f585","year":2019},"citing_paper":{"arxiv_id":"2504.21294","last_updated":"2025-04-30T03:59:58Z","snapshot_observed_at":"2026-08-17T13:31:06.395780Z","submitted_at":"2025-04-30T03:59:58Z","title":"Learning Multi-view Multi-class Anomaly Detection","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-16T05:12:58.382006Z"},"links":{"citing_paper":"/paper/2504.21294"},"observation_digest":"sha256:93da4461cb13e2a36114c492e1e3dc14785b39e4b0393efb0741ba5330938a1c","observation_id":"7ad400a8-378f-44f7-b7b5-789f4356e4b2","resolution":{"observed_at":"2026-08-16T05:12:59.189430Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:12:59.156889Z","title":"Recon- trast: Domain-specific anomaly detection via contrastive reconstruction","venue":null,"work_id":"78d0d628-59b9-425e-8e12-5c5a3cef609a","year":null},"citing_paper":{"arxiv_id":"2504.21294","last_updated":"2025-04-30T03:59:58Z","snapshot_observed_at":"2026-08-17T13:31:06.395780Z","submitted_at":"2025-04-30T03:59:58Z","title":"Learning Multi-view Multi-class Anomaly Detection","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-16T05:12:58.388754Z"},"links":{"citing_paper":"/paper/2504.21294"},"observation_digest":"sha256:2ea66272c9ceacfabd46294676a4c9907969a984061678a5c865bad1ca9f2198","observation_id":"b61e02ee-e6bb-480e-b865-2c9ba3354be4","resolution":{"observed_at":"2026-08-16T05:12:59.164960Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.14325","last_updated":"2025-04-02T12:01:42Z","snapshot_observed_at":"2026-08-16T13:50:19.075903Z","submitted_at":"2024-05-23T08:55:20Z","title":"Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.14325","snapshot_observed_at":"2026-08-16T05:12:58.395204Z","title":"Dinomaly: The less is more philosophy in multi-class unsupervised anomaly detection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.21294","last_updated":"2025-04-30T03:59:58Z","snapshot_observed_at":"2026-08-17T13:31:06.395780Z","submitted_at":"2025-04-30T03:59:58Z","title":"Learning Multi-view Multi-class Anomaly Detection","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T05:12:58.395204Z"},"links":{"cited_paper":"/paper/2405.14325","citing_paper":"/paper/2504.21294"},"observation_digest":"sha256:faac5f2485616778e3e9073858d3b62afc6bca7ca3e3ea15f8cd2bba63b2cc3d","observation_id":"090b8ca3-4925-4eca-9ff4-aace3daa7c83","resolution":{"observed_at":"2026-08-16T05:12:58.395204Z","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":"2026-08-16T05:12:59.128187Z","title":"Mambaad: Exploring state space models for multi-class unsupervised anomaly detection","venue":null,"work_id":"8805b727-bb35-4bca-b5bb-736fc7f08af2","year":2024},"citing_paper":{"arxiv_id":"2504.21294","last_updated":"2025-04-30T03:59:58Z","snapshot_observed_at":"2026-08-17T13:31:06.395780Z","submitted_at":"2025-04-30T03:59:58Z","title":"Learning Multi-view Multi-class Anomaly Detection","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-16T05:12:58.408914Z"},"links":{"citing_paper":"/paper/2504.21294"},"observation_digest":"sha256:c6a6f6a25301617abdf270fbd0301a7e7893ab9d55beee7b695c4f68c860f0da","observation_id":"0f39c82f-2f28-4b23-b981-23bc1ef95dbe","resolution":{"observed_at":"2026-08-16T05:12:59.135944Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:12:59.074683Z","title":"A diffusion- based framework for multi-class anomaly detection","venue":null,"work_id":"80d52337-cfff-497d-8f2a-10ccc318c893","year":2024},"citing_paper":{"arxiv_id":"2504.21294","last_updated":"2025-04-30T03:59:58Z","snapshot_observed_at":"2026-08-17T13:31:06.395780Z","submitted_at":"2025-04-30T03:59:58Z","title":"Learning Multi-view Multi-class Anomaly Detection","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-16T05:12:58.417331Z"},"links":{"citing_paper":"/paper/2504.21294"},"observation_digest":"sha256:4b89f0aeb94f7036fada707bdbb7fa07208ff82e5c4585cdae5a8708a135ffcd","observation_id":"b915b310-7b41-47d5-9973-a47ad7e863f1","resolution":{"observed_at":"2026-08-16T05:12:59.096754Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.11935","last_updated":"2025-08-10T15:26:51Z","snapshot_observed_at":"2026-08-18T11:10:41.178690Z","submitted_at":"2024-07-16T17:26:34Z","title":"Learning Multi-view Anomaly Detection with Efficient Adaptive Selection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.11935","snapshot_observed_at":"2026-08-16T05:12:58.429685Z","title":"Learning multi-view anomaly detection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.21294","last_updated":"2025-04-30T03:59:58Z","snapshot_observed_at":"2026-08-17T13:31:06.395780Z","submitted_at":"2025-04-30T03:59:58Z","title":"Learning Multi-view Multi-class Anomaly Detection","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T05:12:58.429685Z"},"links":{"cited_paper":"/paper/2407.11935","citing_paper":"/paper/2504.21294"},"observation_digest":"sha256:1b407b81268ad73479ed99d06f16f212fbe7b81a010f68169bb34a4a06c63b89","observation_id":"5aeb58f8-18ae-4b8f-b60e-eb1ffa156322","resolution":{"observed_at":"2026-08-16T05:12:58.429685Z","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":"2026-08-16T05:12:59.053980Z","title":"Cut- paste: Self-supervised learning for anomaly detection and localization","venue":null,"work_id":"ec6cf784-1d29-4b97-92cd-946d7b58205c","year":2021},"citing_paper":{"arxiv_id":"2504.21294","last_updated":"2025-04-30T03:59:58Z","snapshot_observed_at":"2026-08-17T13:31:06.395780Z","submitted_at":"2025-04-30T03:59:58Z","title":"Learning Multi-view Multi-class Anomaly Detection","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-16T05:12:58.437941Z"},"links":{"citing_paper":"/paper/2504.21294"},"observation_digest":"sha256:8918f4b31d999e4168b7ed830f68f0698cc154e7338480cb0a6001456200cadb","observation_id":"d0595dbf-fb60-460e-a499-0265dd7d0157","resolution":{"observed_at":"2026-08-16T05:12:59.061377Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:12:59.018894Z","title":"Center- aware adversarial autoencoder for anomaly detection","venue":null,"work_id":"e65bd933-b03a-43ce-b847-bbd8e3888dd2","year":2021},"citing_paper":{"arxiv_id":"2504.21294","last_updated":"2025-04-30T03:59:58Z","snapshot_observed_at":"2026-08-17T13:31:06.395780Z","submitted_at":"2025-04-30T03:59:58Z","title":"Learning Multi-view Multi-class Anomaly Detection","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-16T05:12:58.446478Z"},"links":{"citing_paper":"/paper/2504.21294"},"observation_digest":"sha256:1256dd232ef4d3b9ecceb11087ef3eeab7034d091c8703d4533547e3cbc82c0b","observation_id":"a271a4cf-fa0f-4d76-abed-6ae8077f82b3","resolution":{"observed_at":"2026-08-16T05:12:59.027738Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:12:58.997938Z","title":"Anomaly detection on attributed networks via contrastive self- supervised learning","venue":null,"work_id":"913cd549-244d-4776-a6fa-3f6e3cfe0432","year":2021},"citing_paper":{"arxiv_id":"2504.21294","last_updated":"2025-04-30T03:59:58Z","snapshot_observed_at":"2026-08-17T13:31:06.395780Z","submitted_at":"2025-04-30T03:59:58Z","title":"Learning Multi-view Multi-class Anomaly Detection","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-16T05:12:58.451911Z"},"links":{"citing_paper":"/paper/2504.21294"},"observation_digest":"sha256:1ddeb2b32a04c2c06d989006c45d9e095615a6c8e302317038be472dbb5bbd7f","observation_id":"0e6ac74e-a370-4058-a3af-430d78c21149","resolution":{"observed_at":"2026-08-16T05:12:59.004910Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:12:58.974048Z","title":"Simplenet: A simple network for image anomaly detection and localization","venue":null,"work_id":"79a96bd5-aa79-415e-9cf0-40142178a365","year":2023},"citing_paper":{"arxiv_id":"2504.21294","last_updated":"2025-04-30T03:59:58Z","snapshot_observed_at":"2026-08-17T13:31:06.395780Z","submitted_at":"2025-04-30T03:59:58Z","title":"Learning Multi-view Multi-class Anomaly Detection","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T05:12:58.460572Z"},"links":{"citing_paper":"/paper/2504.21294"},"observation_digest":"sha256:a1d3ac4ab59d98c5b6f0680744c28bdde0814012c363836dac671ae5b45995c4","observation_id":"d7f66249-a9d4-475e-a774-7ce589fca1ca","resolution":{"observed_at":"2026-08-16T05:12:58.982926Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:12:58.943246Z","title":"Zoom in and out: A mixed-scale triplet network for camouflaged object detection","venue":null,"work_id":"bfdfc0d7-f091-433e-b51a-ad63ceb19eaa","year":2022},"citing_paper":{"arxiv_id":"2504.21294","last_updated":"2025-04-30T03:59:58Z","snapshot_observed_at":"2026-08-17T13:31:06.395780Z","submitted_at":"2025-04-30T03:59:58Z","title":"Learning Multi-view Multi-class Anomaly Detection","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-16T05:12:58.468030Z"},"links":{"citing_paper":"/paper/2504.21294"},"observation_digest":"sha256:3b00be91e81149f4240ec9026d7200e088dbb968950e832f2614841bec3618e0","observation_id":"76626e7f-e158-4672-9ef5-08ea6350c9e7","resolution":{"observed_at":"2026-08-16T05:12:58.953145Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:12:58.914418Z","title":"Towards total recall in industrial anomaly detection","venue":null,"work_id":"0ac429bd-7638-4c20-83fd-838033d695bd","year":2022},"citing_paper":{"arxiv_id":"2504.21294","last_updated":"2025-04-30T03:59:58Z","snapshot_observed_at":"2026-08-17T13:31:06.395780Z","submitted_at":"2025-04-30T03:59:58Z","title":"Learning Multi-view Multi-class Anomaly Detection","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-16T05:12:58.475534Z"},"links":{"citing_paper":"/paper/2504.21294"},"observation_digest":"sha256:42f9a628d577175856717ef250cd813a609ee791acc9a170e1a021f527f68ea0","observation_id":"66514bf9-5a57-40fd-8a5c-acef2818b80b","resolution":{"observed_at":"2026-08-16T05:12:58.927777Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:12:58.888347Z","title":"Multi-view convolutional neural networks for 3d shape recogni- tion","venue":null,"work_id":"282f6164-3288-4fad-9879-5ee97e670492","year":2015},"citing_paper":{"arxiv_id":"2504.21294","last_updated":"2025-04-30T03:59:58Z","snapshot_observed_at":"2026-08-17T13:31:06.395780Z","submitted_at":"2025-04-30T03:59:58Z","title":"Learning Multi-view Multi-class Anomaly Detection","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-16T05:12:58.481151Z"},"links":{"citing_paper":"/paper/2504.21294"},"observation_digest":"sha256:9e8638a74d400d808b1096e87de185d698aaca1776f782dcb7940bcb12f8d0f9","observation_id":"53208a42-cd89-4974-a280-16a328584a22","resolution":{"observed_at":"2026-08-16T05:12:58.894317Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:12:58.869433Z","title":"Real-iad: A real-world multi-view dataset for benchmarking versatile industrial anomaly detection","venue":null,"work_id":"df54872a-7272-4b26-92e6-b8b384b78d6b","year":2024},"citing_paper":{"arxiv_id":"2504.21294","last_updated":"2025-04-30T03:59:58Z","snapshot_observed_at":"2026-08-17T13:31:06.395780Z","submitted_at":"2025-04-30T03:59:58Z","title":"Learning Multi-view Multi-class Anomaly Detection","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-16T05:12:58.488631Z"},"links":{"citing_paper":"/paper/2504.21294"},"observation_digest":"sha256:b80c6eb30baa0a4c64409bbd6809271112a6b51a2f7506897f0f48d2a761d072","observation_id":"f7c4baef-1e47-4bfe-b42d-ea02d5848a92","resolution":{"observed_at":"2026-08-16T05:12:58.875053Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:12:58.501881Z","title":"Mvster: Epipolar transformer for efficient multi-view stereo","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2504.21294","last_updated":"2025-04-30T03:59:58Z","snapshot_observed_at":"2026-08-17T13:31:06.395780Z","submitted_at":"2025-04-30T03:59:58Z","title":"Learning Multi-view Multi-class Anomaly Detection","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-16T05:12:58.501881Z"},"links":{"citing_paper":"/paper/2504.21294"},"observation_digest":"sha256:d83fedccfb8f297f8d633afdeae38471c57213c79a5d63108fba67f3e2ce31fe","observation_id":"b5233a34-2e62-475a-8eeb-82ba7f12d246","resolution":{"observed_at":"2026-08-16T05:12:58.501881Z","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":"2026-08-16T05:12:58.837292Z","title":"Aide: A vision-driven multi-view, multi-modal, multi-tasking dataset for assistive driving perception","venue":null,"work_id":"5a1285eb-3992-4a62-8e6d-015735a47141","year":null},"citing_paper":{"arxiv_id":"2504.21294","last_updated":"2025-04-30T03:59:58Z","snapshot_observed_at":"2026-08-17T13:31:06.395780Z","submitted_at":"2025-04-30T03:59:58Z","title":"Learning Multi-view Multi-class Anomaly Detection","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-16T05:12:58.510004Z"},"links":{"citing_paper":"/paper/2504.21294"},"observation_digest":"sha256:bc8c4b88ef78f29b20bf25540af856845ef7098252e42e43b1f0ca5f1d3109f6","observation_id":"169c8051-9e00-4d0d-9a4a-6f2a5b7d60ca","resolution":{"observed_at":"2026-08-16T05:12:58.842915Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:12:58.819540Z","title":"A unified model for multi-class anomaly detection","venue":null,"work_id":"aa625b6e-ae87-41dc-a458-36cb467c1ba9","year":2022},"citing_paper":{"arxiv_id":"2504.21294","last_updated":"2025-04-30T03:59:58Z","snapshot_observed_at":"2026-08-17T13:31:06.395780Z","submitted_at":"2025-04-30T03:59:58Z","title":"Learning Multi-view Multi-class Anomaly Detection","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-16T05:12:58.520553Z"},"links":{"citing_paper":"/paper/2504.21294"},"observation_digest":"sha256:aa450821333fe28bdc79693a9876df64ee8532579d4c9f8f7b95824451b047be","observation_id":"abf9a468-a437-4dc5-86d4-610438bfb619","resolution":{"observed_at":"2026-08-16T05:12:58.826422Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:12:58.795235Z","title":"Tf 2: Few-shot text-free training-free defect image generation for industrial anomaly inspection","venue":null,"work_id":"7e751cbb-28ac-44c3-881b-c8f82c365a3b","year":2024},"citing_paper":{"arxiv_id":"2504.21294","last_updated":"2025-04-30T03:59:58Z","snapshot_observed_at":"2026-08-17T13:31:06.395780Z","submitted_at":"2025-04-30T03:59:58Z","title":"Learning Multi-view Multi-class Anomaly Detection","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-16T05:12:58.527920Z"},"links":{"citing_paper":"/paper/2504.21294"},"observation_digest":"sha256:197bfc6ce566a212928494a81e2bc3fd3de55b43a4bcd5578786bc95458d0a4a","observation_id":"deb2c456-adab-47d5-a037-8156fe105f8a","resolution":{"observed_at":"2026-08-16T05:12:58.806639Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:12:58.764790Z","title":"Draem-a discrimi- natively trained reconstruction embedding for surface anomaly detection","venue":null,"work_id":"aa3a6766-0df8-4964-b6a8-341cd03c1efa","year":2021},"citing_paper":{"arxiv_id":"2504.21294","last_updated":"2025-04-30T03:59:58Z","snapshot_observed_at":"2026-08-17T13:31:06.395780Z","submitted_at":"2025-04-30T03:59:58Z","title":"Learning Multi-view Multi-class Anomaly Detection","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-16T05:12:58.535556Z"},"links":{"citing_paper":"/paper/2504.21294"},"observation_digest":"sha256:32987fec5ffdd4d27901e9c158d9337430f3ada32694e05cabbdad887aa5ef32","observation_id":"98239542-ea8c-4064-a792-42051faff9a6","resolution":{"observed_at":"2026-08-16T05:12:58.777146Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:12:58.736182Z","title":"Reconstruction by inpainting for visual anomaly detection","venue":null,"work_id":"88992d17-5f8f-4b8f-b001-e7459572e876","year":2021},"citing_paper":{"arxiv_id":"2504.21294","last_updated":"2025-04-30T03:59:58Z","snapshot_observed_at":"2026-08-17T13:31:06.395780Z","submitted_at":"2025-04-30T03:59:58Z","title":"Learning Multi-view Multi-class Anomaly Detection","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-16T05:12:58.546191Z"},"links":{"citing_paper":"/paper/2504.21294"},"observation_digest":"sha256:b5b017026e783b3edbf8297b7b1e5bba8302be9d426d47acf851e11b5818b52f","observation_id":"b52faebf-96e6-4116-ba87-8a56e60492d8","resolution":{"observed_at":"2026-08-16T05:12:58.742792Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:12:58.700517Z","title":"Destseg: Segmentation guided denoising student-teacher for anomaly detection","venue":null,"work_id":"440fc2f2-7422-429c-a2b0-98bd4f33af8c","year":2023},"citing_paper":{"arxiv_id":"2504.21294","last_updated":"2025-04-30T03:59:58Z","snapshot_observed_at":"2026-08-17T13:31:06.395780Z","submitted_at":"2025-04-30T03:59:58Z","title":"Learning Multi-view Multi-class Anomaly Detection","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-16T05:12:58.557196Z"},"links":{"citing_paper":"/paper/2504.21294"},"observation_digest":"sha256:38e095fd3550de20093c525ffb7e8ba2817613ca9248418200b0f928e4827c86","observation_id":"6aa52798-3d32-4a3a-9aba-9fd434426e63","resolution":{"observed_at":"2026-08-16T05:12:58.711611Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2504.21294","last_updated":"2025-04-30T03:59:58Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-17T13:31:06.395780Z","submitted_at":"2025-04-30T03:59:58Z","title":"Learning Multi-view Multi-class Anomaly Detection"},"reference_resolution":{"displayed":28,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":4,"verified_exact":0,"verified_fuzzy":24},"total_outbound_references":28},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 1 inbound Pith citation observation for arXiv:2504.21294."}