{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:C6WZA5W6VMVCUFNY45OV2VDOTH","short_pith_number":"pith:C6WZA5W6","canonical_record":{"source":{"id":"2502.00386","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-01T09:58:08Z","cross_cats_sorted":[],"title_canon_sha256":"34d02e4d4881a81a82ebe46ccf3c1d10dd66b60d962a9c2e997b9c492715c1b9","abstract_canon_sha256":"63963e37e054f3adeff1b9e4abd4948214ab39ece47f13cde80189b1c53747ae"},"schema_version":"1.0"},"canonical_sha256":"17ad9076deab2a2a15b8e75d5d546e99c160c163c760a9de7426590dfb99eac2","source":{"kind":"arxiv","id":"2502.00386","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.00386","created_at":"2026-07-05T10:08:33Z"},{"alias_kind":"arxiv_version","alias_value":"2502.00386v1","created_at":"2026-07-05T10:08:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.00386","created_at":"2026-07-05T10:08:33Z"},{"alias_kind":"pith_short_12","alias_value":"C6WZA5W6VMVC","created_at":"2026-07-05T10:08:33Z"},{"alias_kind":"pith_short_16","alias_value":"C6WZA5W6VMVCUFNY","created_at":"2026-07-05T10:08:33Z"},{"alias_kind":"pith_short_8","alias_value":"C6WZA5W6","created_at":"2026-07-05T10:08:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:C6WZA5W6VMVCUFNY45OV2VDOTH","target":"record","payload":{"canonical_record":{"source":{"id":"2502.00386","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-01T09:58:08Z","cross_cats_sorted":[],"title_canon_sha256":"34d02e4d4881a81a82ebe46ccf3c1d10dd66b60d962a9c2e997b9c492715c1b9","abstract_canon_sha256":"63963e37e054f3adeff1b9e4abd4948214ab39ece47f13cde80189b1c53747ae"},"schema_version":"1.0"},"canonical_sha256":"17ad9076deab2a2a15b8e75d5d546e99c160c163c760a9de7426590dfb99eac2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:08:33.031977Z","signature_b64":"wQVf1u5CKZGAWuHa10oDtbsW7Ap/nDrvPdV3bwj3gS+porhZ6j+bK4235G0/1VVnpu4ok268AdUHUYwqbzhqDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"17ad9076deab2a2a15b8e75d5d546e99c160c163c760a9de7426590dfb99eac2","last_reissued_at":"2026-07-05T10:08:33.031568Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:08:33.031568Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.00386","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:08:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cS+8jQliSb4scrXAfHN7xp66EweT6DlIhE3A4lz/drg1n3xLLpsXSNcX5cgSDGChMPDLl7MOPirKPm/AFr/0Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T15:50:09.170727Z"},"content_sha256":"e2911c7b63f7c0a19936ff4a648853627b6e9e465df5b03d96243c85c7b3fdfb","schema_version":"1.0","event_id":"sha256:e2911c7b63f7c0a19936ff4a648853627b6e9e465df5b03d96243c85c7b3fdfb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:C6WZA5W6VMVCUFNY45OV2VDOTH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient Adaptive Label Refinement for Label Noise Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bo Zhou, Debo Cheng, Guangquan Lu, Jiaye Li, Shichao Zhang, Wenzhen Zhang","submitted_at":"2025-02-01T09:58:08Z","abstract_excerpt":"Deep neural networks are highly susceptible to overfitting noisy labels, which leads to degraded performance. Existing methods address this issue by employing manually defined criteria, aiming to achieve optimal partitioning in each iteration to avoid fitting noisy labels while thoroughly learning clean samples. However, this often results in overly complex and difficult-to-train models. To address this issue, we decouple the tasks of avoiding fitting incorrect labels and thoroughly learning clean samples and propose a simple yet highly applicable method called Adaptive Label Refinement (ALR)."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.00386","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/2502.00386/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:08:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"m9BRAhtHIGYbyRCp2zZ9EkP4QB3d2Nm0gcR8YfKoI1BL07zjAujoPK1vPo1sCwHcsg9eQP1AQkw9mWxe0jt8BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T15:50:09.171328Z"},"content_sha256":"e7ad8176b5c357c6a9f1c43616dee827f835a375aa0b80ea889b47b2ac06645a","schema_version":"1.0","event_id":"sha256:e7ad8176b5c357c6a9f1c43616dee827f835a375aa0b80ea889b47b2ac06645a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/C6WZA5W6VMVCUFNY45OV2VDOTH/bundle.json","state_url":"https://pith.science/pith/C6WZA5W6VMVCUFNY45OV2VDOTH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/C6WZA5W6VMVCUFNY45OV2VDOTH/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-11T15:50:09Z","links":{"resolver":"https://pith.science/pith/C6WZA5W6VMVCUFNY45OV2VDOTH","bundle":"https://pith.science/pith/C6WZA5W6VMVCUFNY45OV2VDOTH/bundle.json","state":"https://pith.science/pith/C6WZA5W6VMVCUFNY45OV2VDOTH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/C6WZA5W6VMVCUFNY45OV2VDOTH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:C6WZA5W6VMVCUFNY45OV2VDOTH","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"63963e37e054f3adeff1b9e4abd4948214ab39ece47f13cde80189b1c53747ae","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-01T09:58:08Z","title_canon_sha256":"34d02e4d4881a81a82ebe46ccf3c1d10dd66b60d962a9c2e997b9c492715c1b9"},"schema_version":"1.0","source":{"id":"2502.00386","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.00386","created_at":"2026-07-05T10:08:33Z"},{"alias_kind":"arxiv_version","alias_value":"2502.00386v1","created_at":"2026-07-05T10:08:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.00386","created_at":"2026-07-05T10:08:33Z"},{"alias_kind":"pith_short_12","alias_value":"C6WZA5W6VMVC","created_at":"2026-07-05T10:08:33Z"},{"alias_kind":"pith_short_16","alias_value":"C6WZA5W6VMVCUFNY","created_at":"2026-07-05T10:08:33Z"},{"alias_kind":"pith_short_8","alias_value":"C6WZA5W6","created_at":"2026-07-05T10:08:33Z"}],"graph_snapshots":[{"event_id":"sha256:e7ad8176b5c357c6a9f1c43616dee827f835a375aa0b80ea889b47b2ac06645a","target":"graph","created_at":"2026-07-05T10:08:33Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2502.00386/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep neural networks are highly susceptible to overfitting noisy labels, which leads to degraded performance. Existing methods address this issue by employing manually defined criteria, aiming to achieve optimal partitioning in each iteration to avoid fitting noisy labels while thoroughly learning clean samples. However, this often results in overly complex and difficult-to-train models. To address this issue, we decouple the tasks of avoiding fitting incorrect labels and thoroughly learning clean samples and propose a simple yet highly applicable method called Adaptive Label Refinement (ALR).","authors_text":"Bo Zhou, Debo Cheng, Guangquan Lu, Jiaye Li, Shichao Zhang, Wenzhen Zhang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-01T09:58:08Z","title":"Efficient Adaptive Label Refinement for Label Noise Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.00386","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:e2911c7b63f7c0a19936ff4a648853627b6e9e465df5b03d96243c85c7b3fdfb","target":"record","created_at":"2026-07-05T10:08:33Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"63963e37e054f3adeff1b9e4abd4948214ab39ece47f13cde80189b1c53747ae","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-01T09:58:08Z","title_canon_sha256":"34d02e4d4881a81a82ebe46ccf3c1d10dd66b60d962a9c2e997b9c492715c1b9"},"schema_version":"1.0","source":{"id":"2502.00386","kind":"arxiv","version":1}},"canonical_sha256":"17ad9076deab2a2a15b8e75d5d546e99c160c163c760a9de7426590dfb99eac2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"17ad9076deab2a2a15b8e75d5d546e99c160c163c760a9de7426590dfb99eac2","first_computed_at":"2026-07-05T10:08:33.031568Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:08:33.031568Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wQVf1u5CKZGAWuHa10oDtbsW7Ap/nDrvPdV3bwj3gS+porhZ6j+bK4235G0/1VVnpu4ok268AdUHUYwqbzhqDw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:08:33.031977Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.00386","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e2911c7b63f7c0a19936ff4a648853627b6e9e465df5b03d96243c85c7b3fdfb","sha256:e7ad8176b5c357c6a9f1c43616dee827f835a375aa0b80ea889b47b2ac06645a"],"state_sha256":"86b66a5893c21c6a8fc14a7ad7c3b15e5913be60cb434c40b75a57b755370f24"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kxJmWVKAo/Bg+2lon4BnlsbVEOQdej4DGoXu9AceYJ8rGdaElNmZI3uvdgmw0U1kHRWkp5m5UBgASqUifXrFBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T15:50:09.177358Z","bundle_sha256":"ee93171df948a18d74bb2cc2a356b4af04c84f71bd6e79dce77c213eee4912f4"}}