{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:XI4HTH2AVAHQZRCYOJIZPPA4EG","short_pith_number":"pith:XI4HTH2A","schema_version":"1.0","canonical_sha256":"ba38799f40a80f0cc458725197bc1c2187bbb049d045c332616dae7b96a7343b","source":{"kind":"arxiv","id":"2501.04527","version":1},"attestation_state":"computed","paper":{"title":"Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Hongtao Yu, Hongxin Zhi, Shaome Li, Xiuming Zhao, Yiteng Wu","submitted_at":"2025-01-08T14:19:03Z","abstract_excerpt":"Adversarial training has proven to be a highly effective method for improving the robustness of deep neural networks against adversarial attacks. Nonetheless, it has been observed to exhibit a limitation in terms of robust fairness, characterized by a significant disparity in robustness across different classes. Recent efforts to mitigate this problem have turned to class-wise reweighted methods. However, these methods suffer from a lack of rigorous theoretical analysis and are limited in their exploration of the weight space, as they mainly rely on existing heuristic algorithms or intuition t"},"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":"2501.04527","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-01-08T14:19:03Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"b7a21f9ba63fc11f9ee908d58ff506e5b06dbc85114cb5474562ebe79d079c3e","abstract_canon_sha256":"a0e31bf36440b9f3b7e617e325db204ad6fce152c06cc12657544e6912de17db"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:58:36.523012Z","signature_b64":"H46a9jDcJhwjeM9eZng4Ye8b2LIEdJL6UaE08pMxDUJmZllzVvBn/jW6QHrT7u7MPzYUSUIve/b4QRjDZ34dCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ba38799f40a80f0cc458725197bc1c2187bbb049d045c332616dae7b96a7343b","last_reissued_at":"2026-07-05T09:58:36.521945Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:58:36.521945Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Hongtao Yu, Hongxin Zhi, Shaome Li, Xiuming Zhao, Yiteng Wu","submitted_at":"2025-01-08T14:19:03Z","abstract_excerpt":"Adversarial training has proven to be a highly effective method for improving the robustness of deep neural networks against adversarial attacks. Nonetheless, it has been observed to exhibit a limitation in terms of robust fairness, characterized by a significant disparity in robustness across different classes. Recent efforts to mitigate this problem have turned to class-wise reweighted methods. However, these methods suffer from a lack of rigorous theoretical analysis and are limited in their exploration of the weight space, as they mainly rely on existing heuristic algorithms or intuition t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.04527","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/2501.04527/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":"2501.04527","created_at":"2026-07-05T09:58:36.522402+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.04527v1","created_at":"2026-07-05T09:58:36.522402+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.04527","created_at":"2026-07-05T09:58:36.522402+00:00"},{"alias_kind":"pith_short_12","alias_value":"XI4HTH2AVAHQ","created_at":"2026-07-05T09:58:36.522402+00:00"},{"alias_kind":"pith_short_16","alias_value":"XI4HTH2AVAHQZRCY","created_at":"2026-07-05T09:58:36.522402+00:00"},{"alias_kind":"pith_short_8","alias_value":"XI4HTH2A","created_at":"2026-07-05T09:58:36.522402+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/XI4HTH2AVAHQZRCYOJIZPPA4EG","json":"https://pith.science/pith/XI4HTH2AVAHQZRCYOJIZPPA4EG.json","graph_json":"https://pith.science/api/pith-number/XI4HTH2AVAHQZRCYOJIZPPA4EG/graph.json","events_json":"https://pith.science/api/pith-number/XI4HTH2AVAHQZRCYOJIZPPA4EG/events.json","paper":"https://pith.science/paper/XI4HTH2A"},"agent_actions":{"view_html":"https://pith.science/pith/XI4HTH2AVAHQZRCYOJIZPPA4EG","download_json":"https://pith.science/pith/XI4HTH2AVAHQZRCYOJIZPPA4EG.json","view_paper":"https://pith.science/paper/XI4HTH2A","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.04527&json=true","fetch_graph":"https://pith.science/api/pith-number/XI4HTH2AVAHQZRCYOJIZPPA4EG/graph.json","fetch_events":"https://pith.science/api/pith-number/XI4HTH2AVAHQZRCYOJIZPPA4EG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XI4HTH2AVAHQZRCYOJIZPPA4EG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XI4HTH2AVAHQZRCYOJIZPPA4EG/action/storage_attestation","attest_author":"https://pith.science/pith/XI4HTH2AVAHQZRCYOJIZPPA4EG/action/author_attestation","sign_citation":"https://pith.science/pith/XI4HTH2AVAHQZRCYOJIZPPA4EG/action/citation_signature","submit_replication":"https://pith.science/pith/XI4HTH2AVAHQZRCYOJIZPPA4EG/action/replication_record"}},"created_at":"2026-07-05T09:58:36.522402+00:00","updated_at":"2026-07-05T09:58:36.522402+00:00"}