{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:IUWAZEPHBL46VNAGATU46W6MY5","short_pith_number":"pith:IUWAZEPH","schema_version":"1.0","canonical_sha256":"452c0c91e70af9eab40604e9cf5bccc753429de92e0f2a9b65894f22df280b3a","source":{"kind":"arxiv","id":"2205.14852","version":1},"attestation_state":"computed","paper":{"title":"Benchmarking Unsupervised Anomaly Detection and Localization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Liwei Wu, Wei Li, Xiang Wang, Ye Zheng, Yu Qi","submitted_at":"2022-05-30T04:57:25Z","abstract_excerpt":"Unsupervised anomaly detection and localization, as of one the most practical and challenging problems in computer vision, has received great attention in recent years. From the time the MVTec AD dataset was proposed to the present, new research methods that are constantly being proposed push its precision to saturation. It is the time to conduct a comprehensive comparison of existing methods to inspire further research. This paper extensively compares 13 papers in terms of the performance in unsupervised anomaly detection and localization tasks, and adds a comparison of inference efficiency p"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2205.14852","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-05-30T04:57:25Z","cross_cats_sorted":[],"title_canon_sha256":"7abcf3a0051914bdaccaf04fab305a71dd912dd554cbce32a637aec27dfe0984","abstract_canon_sha256":"284eabf80e1b64c180ad51870ff61a55ed0a9e172611113a79298a6c17f2eb7d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:27:21.836086Z","signature_b64":"PYyyz9G8pO2cxABgxDNLg02IsxpfDTmHZNadJj5/i0c7SdBC0JVFn7g8w6h50i2IXKKfs2XoHJH3uJNyD0X8Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"452c0c91e70af9eab40604e9cf5bccc753429de92e0f2a9b65894f22df280b3a","last_reissued_at":"2026-07-05T04:27:21.835586Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:27:21.835586Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Benchmarking Unsupervised Anomaly Detection and Localization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Liwei Wu, Wei Li, Xiang Wang, Ye Zheng, Yu Qi","submitted_at":"2022-05-30T04:57:25Z","abstract_excerpt":"Unsupervised anomaly detection and localization, as of one the most practical and challenging problems in computer vision, has received great attention in recent years. From the time the MVTec AD dataset was proposed to the present, new research methods that are constantly being proposed push its precision to saturation. It is the time to conduct a comprehensive comparison of existing methods to inspire further research. This paper extensively compares 13 papers in terms of the performance in unsupervised anomaly detection and localization tasks, and adds a comparison of inference efficiency p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.14852","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2205.14852/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2205.14852","created_at":"2026-07-05T04:27:21.835651+00:00"},{"alias_kind":"arxiv_version","alias_value":"2205.14852v1","created_at":"2026-07-05T04:27:21.835651+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.14852","created_at":"2026-07-05T04:27:21.835651+00:00"},{"alias_kind":"pith_short_12","alias_value":"IUWAZEPHBL46","created_at":"2026-07-05T04:27:21.835651+00:00"},{"alias_kind":"pith_short_16","alias_value":"IUWAZEPHBL46VNAG","created_at":"2026-07-05T04:27:21.835651+00:00"},{"alias_kind":"pith_short_8","alias_value":"IUWAZEPH","created_at":"2026-07-05T04:27:21.835651+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2409.15980","citing_title":"Leveraging Unsupervised Learning for Cost-Effective Visual Anomaly Detection","ref_index":30,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IUWAZEPHBL46VNAGATU46W6MY5","json":"https://pith.science/pith/IUWAZEPHBL46VNAGATU46W6MY5.json","graph_json":"https://pith.science/api/pith-number/IUWAZEPHBL46VNAGATU46W6MY5/graph.json","events_json":"https://pith.science/api/pith-number/IUWAZEPHBL46VNAGATU46W6MY5/events.json","paper":"https://pith.science/paper/IUWAZEPH"},"agent_actions":{"view_html":"https://pith.science/pith/IUWAZEPHBL46VNAGATU46W6MY5","download_json":"https://pith.science/pith/IUWAZEPHBL46VNAGATU46W6MY5.json","view_paper":"https://pith.science/paper/IUWAZEPH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2205.14852&json=true","fetch_graph":"https://pith.science/api/pith-number/IUWAZEPHBL46VNAGATU46W6MY5/graph.json","fetch_events":"https://pith.science/api/pith-number/IUWAZEPHBL46VNAGATU46W6MY5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IUWAZEPHBL46VNAGATU46W6MY5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IUWAZEPHBL46VNAGATU46W6MY5/action/storage_attestation","attest_author":"https://pith.science/pith/IUWAZEPHBL46VNAGATU46W6MY5/action/author_attestation","sign_citation":"https://pith.science/pith/IUWAZEPHBL46VNAGATU46W6MY5/action/citation_signature","submit_replication":"https://pith.science/pith/IUWAZEPHBL46VNAGATU46W6MY5/action/replication_record"}},"created_at":"2026-07-05T04:27:21.835651+00:00","updated_at":"2026-07-05T04:27:21.835651+00:00"}