{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:FMWX4JK3F46ANLZHGYX7ALHINT","short_pith_number":"pith:FMWX4JK3","schema_version":"1.0","canonical_sha256":"2b2d7e255b2f3c06af27362ff02ce86cdac1bce19a8fdd5a82d4ffd2b26c6ecf","source":{"kind":"arxiv","id":"2307.13239","version":1},"attestation_state":"computed","paper":{"title":"RoSAS: Deep Semi-Supervised Anomaly Detection with Contamination-Resilient Continuous Supervision","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Guansong Pang, Hongzuo Xu, Ning Liu, Songlei Jian, Yijie Wang, Yongjun Wang","submitted_at":"2023-07-25T04:04:49Z","abstract_excerpt":"Semi-supervised anomaly detection methods leverage a few anomaly examples to yield drastically improved performance compared to unsupervised models. However, they still suffer from two limitations: 1) unlabeled anomalies (i.e., anomaly contamination) may mislead the learning process when all the unlabeled data are employed as inliers for model training; 2) only discrete supervision information (such as binary or ordinal data labels) is exploited, which leads to suboptimal learning of anomaly scores that essentially take on a continuous distribution. Therefore, this paper proposes a novel semi-"},"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":"2307.13239","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-07-25T04:04:49Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"30e03047c22a19065179a0ae7dbcc80a2bfe1a70a821105bc67354c381db7665","abstract_canon_sha256":"6a808131b0bb379c671119a6106552bd21804e19cc7aa615cd5a53786a8e0db5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:34:11.167100Z","signature_b64":"rbywzV8mA9DoFdTU1wfKoDhyFiDP/lQC6nw/sU0mbNUNEN32ka4HRsbIQwYi7QcWWoGytb/OaJSmdb+/C1IwDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2b2d7e255b2f3c06af27362ff02ce86cdac1bce19a8fdd5a82d4ffd2b26c6ecf","last_reissued_at":"2026-07-05T06:34:11.166627Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:34:11.166627Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RoSAS: Deep Semi-Supervised Anomaly Detection with Contamination-Resilient Continuous Supervision","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Guansong Pang, Hongzuo Xu, Ning Liu, Songlei Jian, Yijie Wang, Yongjun Wang","submitted_at":"2023-07-25T04:04:49Z","abstract_excerpt":"Semi-supervised anomaly detection methods leverage a few anomaly examples to yield drastically improved performance compared to unsupervised models. However, they still suffer from two limitations: 1) unlabeled anomalies (i.e., anomaly contamination) may mislead the learning process when all the unlabeled data are employed as inliers for model training; 2) only discrete supervision information (such as binary or ordinal data labels) is exploited, which leads to suboptimal learning of anomaly scores that essentially take on a continuous distribution. Therefore, this paper proposes a novel semi-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.13239","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/2307.13239/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":"2307.13239","created_at":"2026-07-05T06:34:11.166696+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.13239v1","created_at":"2026-07-05T06:34:11.166696+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.13239","created_at":"2026-07-05T06:34:11.166696+00:00"},{"alias_kind":"pith_short_12","alias_value":"FMWX4JK3F46A","created_at":"2026-07-05T06:34:11.166696+00:00"},{"alias_kind":"pith_short_16","alias_value":"FMWX4JK3F46ANLZH","created_at":"2026-07-05T06:34:11.166696+00:00"},{"alias_kind":"pith_short_8","alias_value":"FMWX4JK3","created_at":"2026-07-05T06:34:11.166696+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FMWX4JK3F46ANLZHGYX7ALHINT","json":"https://pith.science/pith/FMWX4JK3F46ANLZHGYX7ALHINT.json","graph_json":"https://pith.science/api/pith-number/FMWX4JK3F46ANLZHGYX7ALHINT/graph.json","events_json":"https://pith.science/api/pith-number/FMWX4JK3F46ANLZHGYX7ALHINT/events.json","paper":"https://pith.science/paper/FMWX4JK3"},"agent_actions":{"view_html":"https://pith.science/pith/FMWX4JK3F46ANLZHGYX7ALHINT","download_json":"https://pith.science/pith/FMWX4JK3F46ANLZHGYX7ALHINT.json","view_paper":"https://pith.science/paper/FMWX4JK3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.13239&json=true","fetch_graph":"https://pith.science/api/pith-number/FMWX4JK3F46ANLZHGYX7ALHINT/graph.json","fetch_events":"https://pith.science/api/pith-number/FMWX4JK3F46ANLZHGYX7ALHINT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FMWX4JK3F46ANLZHGYX7ALHINT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FMWX4JK3F46ANLZHGYX7ALHINT/action/storage_attestation","attest_author":"https://pith.science/pith/FMWX4JK3F46ANLZHGYX7ALHINT/action/author_attestation","sign_citation":"https://pith.science/pith/FMWX4JK3F46ANLZHGYX7ALHINT/action/citation_signature","submit_replication":"https://pith.science/pith/FMWX4JK3F46ANLZHGYX7ALHINT/action/replication_record"}},"created_at":"2026-07-05T06:34:11.166696+00:00","updated_at":"2026-07-05T06:34:11.166696+00:00"}