{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:QRNUMDT2OL46O6VD2IK6EZJYIB","short_pith_number":"pith:QRNUMDT2","schema_version":"1.0","canonical_sha256":"845b460e7a72f9e77aa3d215e26538407dcf6aca41ad2f1c18339ca0218a03c1","source":{"kind":"arxiv","id":"2411.16110","version":1},"attestation_state":"computed","paper":{"title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Je Hyeong Hong, Jiin Im, Yongho Son","submitted_at":"2024-11-25T05:51:38Z","abstract_excerpt":"While the mainstream research in anomaly detection has mainly followed the one-class classification, practical industrial environments often incur noisy training data due to annotation errors or lack of labels for new or refurbished products. To address these issues, we propose a novel learning-based approach for fully unsupervised anomaly detection with unlabeled and potentially contaminated training data. Our method is motivated by two observations, that i) the pairwise feature distances between the normal samples are on average likely to be smaller than those between the anomaly samples or "},"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":"2411.16110","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-25T05:51:38Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"e41786ba4da83f20f2ede249adbb708031305c0b0ef119767edf9426f384cc91","abstract_canon_sha256":"c69a588116635a0269b49e4da6044b8e850e2500a1c26fed3a35a42a9485438b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:40:04.287304Z","signature_b64":"UMIg0+/zExXvpjvCh4NcX8Z1g2DoeKUT0xJCuEBQ65gSl0PcSNH6WUTRtvi16i7zfmTPETVPaU1Oy2A2X96cAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"845b460e7a72f9e77aa3d215e26538407dcf6aca41ad2f1c18339ca0218a03c1","last_reissued_at":"2026-07-05T09:40:04.286892Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:40:04.286892Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Je Hyeong Hong, Jiin Im, Yongho Son","submitted_at":"2024-11-25T05:51:38Z","abstract_excerpt":"While the mainstream research in anomaly detection has mainly followed the one-class classification, practical industrial environments often incur noisy training data due to annotation errors or lack of labels for new or refurbished products. To address these issues, we propose a novel learning-based approach for fully unsupervised anomaly detection with unlabeled and potentially contaminated training data. Our method is motivated by two observations, that i) the pairwise feature distances between the normal samples are on average likely to be smaller than those between the anomaly samples or "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.16110","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/2411.16110/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":"2411.16110","created_at":"2026-07-05T09:40:04.286948+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.16110v1","created_at":"2026-07-05T09:40:04.286948+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.16110","created_at":"2026-07-05T09:40:04.286948+00:00"},{"alias_kind":"pith_short_12","alias_value":"QRNUMDT2OL46","created_at":"2026-07-05T09:40:04.286948+00:00"},{"alias_kind":"pith_short_16","alias_value":"QRNUMDT2OL46O6VD","created_at":"2026-07-05T09:40:04.286948+00:00"},{"alias_kind":"pith_short_8","alias_value":"QRNUMDT2","created_at":"2026-07-05T09:40:04.286948+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.01924","citing_title":"Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection","ref_index":21,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QRNUMDT2OL46O6VD2IK6EZJYIB","json":"https://pith.science/pith/QRNUMDT2OL46O6VD2IK6EZJYIB.json","graph_json":"https://pith.science/api/pith-number/QRNUMDT2OL46O6VD2IK6EZJYIB/graph.json","events_json":"https://pith.science/api/pith-number/QRNUMDT2OL46O6VD2IK6EZJYIB/events.json","paper":"https://pith.science/paper/QRNUMDT2"},"agent_actions":{"view_html":"https://pith.science/pith/QRNUMDT2OL46O6VD2IK6EZJYIB","download_json":"https://pith.science/pith/QRNUMDT2OL46O6VD2IK6EZJYIB.json","view_paper":"https://pith.science/paper/QRNUMDT2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.16110&json=true","fetch_graph":"https://pith.science/api/pith-number/QRNUMDT2OL46O6VD2IK6EZJYIB/graph.json","fetch_events":"https://pith.science/api/pith-number/QRNUMDT2OL46O6VD2IK6EZJYIB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QRNUMDT2OL46O6VD2IK6EZJYIB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QRNUMDT2OL46O6VD2IK6EZJYIB/action/storage_attestation","attest_author":"https://pith.science/pith/QRNUMDT2OL46O6VD2IK6EZJYIB/action/author_attestation","sign_citation":"https://pith.science/pith/QRNUMDT2OL46O6VD2IK6EZJYIB/action/citation_signature","submit_replication":"https://pith.science/pith/QRNUMDT2OL46O6VD2IK6EZJYIB/action/replication_record"}},"created_at":"2026-07-05T09:40:04.286948+00:00","updated_at":"2026-07-05T09:40:04.286948+00:00"}