{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:CT2NJQ3PLXD4PKCVVZ5L7JMHKA","short_pith_number":"pith:CT2NJQ3P","schema_version":"1.0","canonical_sha256":"14f4d4c36f5dc7c7a855ae7abfa5875026c14da9790741a615fd6c0526ddbb1b","source":{"kind":"arxiv","id":"2407.01905","version":1},"attestation_state":"computed","paper":{"title":"Enhancing Multi-Class Anomaly Detection via Diffusion Refinement with Dual Conditioning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bin-Bin Gao, Chengjie Wang, Jiawei Zhan, Jinxiang Lai, Jun Liu, Xiaochen Chen","submitted_at":"2024-07-02T03:09:40Z","abstract_excerpt":"Anomaly detection, the technique of identifying abnormal samples using only normal samples, has attracted widespread interest in industry. Existing one-model-per-category methods often struggle with limited generalization capabilities due to their focus on a single category, and can fail when encountering variations in product. Recent feature reconstruction methods, as representatives in one-model-all-categories schemes, face challenges including reconstructing anomalous samples and blurry reconstructions. In this paper, we creatively combine a diffusion model and a transformer for multi-class"},"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":"2407.01905","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-07-02T03:09:40Z","cross_cats_sorted":[],"title_canon_sha256":"673ff9aed529ca7f52e89d103bc83e7e92b74aa83ca38eda62a341d70df9fe4a","abstract_canon_sha256":"45f1f55c7d4607009ee4eabcd3b02feb52e681059abe257fdc47d16af1001a2f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:39:07.038502Z","signature_b64":"g01t/K3JQkKzB7XqRjQkhdYzGmIJeH4AYmG2NE8J2BazNhjAOFcexFa300CSQCTSZtAWSb5xe1lWHl+aMUuqDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"14f4d4c36f5dc7c7a855ae7abfa5875026c14da9790741a615fd6c0526ddbb1b","last_reissued_at":"2026-07-05T08:39:07.038051Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:39:07.038051Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enhancing Multi-Class Anomaly Detection via Diffusion Refinement with Dual Conditioning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bin-Bin Gao, Chengjie Wang, Jiawei Zhan, Jinxiang Lai, Jun Liu, Xiaochen Chen","submitted_at":"2024-07-02T03:09:40Z","abstract_excerpt":"Anomaly detection, the technique of identifying abnormal samples using only normal samples, has attracted widespread interest in industry. Existing one-model-per-category methods often struggle with limited generalization capabilities due to their focus on a single category, and can fail when encountering variations in product. Recent feature reconstruction methods, as representatives in one-model-all-categories schemes, face challenges including reconstructing anomalous samples and blurry reconstructions. In this paper, we creatively combine a diffusion model and a transformer for multi-class"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.01905","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/2407.01905/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":"2407.01905","created_at":"2026-07-05T08:39:07.038108+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.01905v1","created_at":"2026-07-05T08:39:07.038108+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.01905","created_at":"2026-07-05T08:39:07.038108+00:00"},{"alias_kind":"pith_short_12","alias_value":"CT2NJQ3PLXD4","created_at":"2026-07-05T08:39:07.038108+00:00"},{"alias_kind":"pith_short_16","alias_value":"CT2NJQ3PLXD4PKCV","created_at":"2026-07-05T08:39:07.038108+00:00"},{"alias_kind":"pith_short_8","alias_value":"CT2NJQ3P","created_at":"2026-07-05T08:39:07.038108+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.11430","citing_title":"A Survey on Diffusion Models for Anomaly Detection","ref_index":40,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CT2NJQ3PLXD4PKCVVZ5L7JMHKA","json":"https://pith.science/pith/CT2NJQ3PLXD4PKCVVZ5L7JMHKA.json","graph_json":"https://pith.science/api/pith-number/CT2NJQ3PLXD4PKCVVZ5L7JMHKA/graph.json","events_json":"https://pith.science/api/pith-number/CT2NJQ3PLXD4PKCVVZ5L7JMHKA/events.json","paper":"https://pith.science/paper/CT2NJQ3P"},"agent_actions":{"view_html":"https://pith.science/pith/CT2NJQ3PLXD4PKCVVZ5L7JMHKA","download_json":"https://pith.science/pith/CT2NJQ3PLXD4PKCVVZ5L7JMHKA.json","view_paper":"https://pith.science/paper/CT2NJQ3P","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.01905&json=true","fetch_graph":"https://pith.science/api/pith-number/CT2NJQ3PLXD4PKCVVZ5L7JMHKA/graph.json","fetch_events":"https://pith.science/api/pith-number/CT2NJQ3PLXD4PKCVVZ5L7JMHKA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CT2NJQ3PLXD4PKCVVZ5L7JMHKA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CT2NJQ3PLXD4PKCVVZ5L7JMHKA/action/storage_attestation","attest_author":"https://pith.science/pith/CT2NJQ3PLXD4PKCVVZ5L7JMHKA/action/author_attestation","sign_citation":"https://pith.science/pith/CT2NJQ3PLXD4PKCVVZ5L7JMHKA/action/citation_signature","submit_replication":"https://pith.science/pith/CT2NJQ3PLXD4PKCVVZ5L7JMHKA/action/replication_record"}},"created_at":"2026-07-05T08:39:07.038108+00:00","updated_at":"2026-07-05T08:39:07.038108+00:00"}