{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:2AUFXKIY5365MKDTTG72C37BIQ","short_pith_number":"pith:2AUFXKIY","canonical_record":{"source":{"id":"2501.04269","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-08T04:37:36Z","cross_cats_sorted":[],"title_canon_sha256":"17ac90d46bb9dc8e2d8ce24fbdd16761adc883a97b670c272b8f8b7786540c7c","abstract_canon_sha256":"16ae72b117e150052575c486e8b811ec64af5674d0950073163b83afc0ba094a"},"schema_version":"1.0"},"canonical_sha256":"d0285ba918eefdd6287399bfa16fe144117acb52952cc19e4d1e1d2d6ba0e08f","source":{"kind":"arxiv","id":"2501.04269","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.04269","created_at":"2026-07-05T09:58:32Z"},{"alias_kind":"arxiv_version","alias_value":"2501.04269v1","created_at":"2026-07-05T09:58:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.04269","created_at":"2026-07-05T09:58:32Z"},{"alias_kind":"pith_short_12","alias_value":"2AUFXKIY5365","created_at":"2026-07-05T09:58:32Z"},{"alias_kind":"pith_short_16","alias_value":"2AUFXKIY5365MKDT","created_at":"2026-07-05T09:58:32Z"},{"alias_kind":"pith_short_8","alias_value":"2AUFXKIY","created_at":"2026-07-05T09:58:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:2AUFXKIY5365MKDTTG72C37BIQ","target":"record","payload":{"canonical_record":{"source":{"id":"2501.04269","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-08T04:37:36Z","cross_cats_sorted":[],"title_canon_sha256":"17ac90d46bb9dc8e2d8ce24fbdd16761adc883a97b670c272b8f8b7786540c7c","abstract_canon_sha256":"16ae72b117e150052575c486e8b811ec64af5674d0950073163b83afc0ba094a"},"schema_version":"1.0"},"canonical_sha256":"d0285ba918eefdd6287399bfa16fe144117acb52952cc19e4d1e1d2d6ba0e08f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:58:32.549676Z","signature_b64":"PP34mMd26iQxRFz5sXnWAml6yc0itleuhKAO6EeiAscwB4G7mUHQ4yK6ho/Iu+xTuN4mmYgyRh5FiLfrde8wCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d0285ba918eefdd6287399bfa16fe144117acb52952cc19e4d1e1d2d6ba0e08f","last_reissued_at":"2026-07-05T09:58:32.549251Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:58:32.549251Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.04269","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":"2026-07-05T09:58:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nnyTfzpseN8/vuQYdztIbXGCVV4dtgdWfq8qiHrz+yM7kGJzcIwbrA3ThK/1WsQn2YKcTbD1GZDoaViuwpNQAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T04:39:09.353132Z"},"content_sha256":"4f0c9d07a2f5ad06e3e2b362c1f2b95c191d570ecf19f5219bed5e713a6f1d49","schema_version":"1.0","event_id":"sha256:4f0c9d07a2f5ad06e3e2b362c1f2b95c191d570ecf19f5219bed5e713a6f1d49"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:2AUFXKIY5365MKDTTG72C37BIQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Open set label noise learning with robust sample selection and margin-guided module","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Liyan Ma, Qianxi Xia, Shihui Ying, Yang Sun, Yuandi Zhao, Zhijie Wen","submitted_at":"2025-01-08T04:37:36Z","abstract_excerpt":"In recent years, the remarkable success of deep neural networks (DNNs) in computer vision is largely due to large-scale, high-quality labeled datasets. Training directly on real-world datasets with label noise may result in overfitting. The traditional method is limited to deal with closed set label noise, where noisy training data has true class labels within the known label space. However, there are some real-world datasets containing open set label noise, which means that some samples belong to an unknown class outside the known label space. To address the open set label noise problem, we i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.04269","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/2501.04269/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":"2026-07-05T09:58:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LdkKU/SdwCHsTEsEeTVGd+fsaedj2gzlSrtFC4/r7eHhP663VE2mXUOPzKXq4tyLts97MmnP7vuk0+p8i/YSBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T04:39:09.353690Z"},"content_sha256":"7099d7c3269844e47ba041d07a79922edbb3233f9620289ffd7e953b5b6e7185","schema_version":"1.0","event_id":"sha256:7099d7c3269844e47ba041d07a79922edbb3233f9620289ffd7e953b5b6e7185"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2AUFXKIY5365MKDTTG72C37BIQ/bundle.json","state_url":"https://pith.science/pith/2AUFXKIY5365MKDTTG72C37BIQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2AUFXKIY5365MKDTTG72C37BIQ/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":"2026-08-11T04:39:09Z","links":{"resolver":"https://pith.science/pith/2AUFXKIY5365MKDTTG72C37BIQ","bundle":"https://pith.science/pith/2AUFXKIY5365MKDTTG72C37BIQ/bundle.json","state":"https://pith.science/pith/2AUFXKIY5365MKDTTG72C37BIQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2AUFXKIY5365MKDTTG72C37BIQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:2AUFXKIY5365MKDTTG72C37BIQ","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":"16ae72b117e150052575c486e8b811ec64af5674d0950073163b83afc0ba094a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-08T04:37:36Z","title_canon_sha256":"17ac90d46bb9dc8e2d8ce24fbdd16761adc883a97b670c272b8f8b7786540c7c"},"schema_version":"1.0","source":{"id":"2501.04269","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.04269","created_at":"2026-07-05T09:58:32Z"},{"alias_kind":"arxiv_version","alias_value":"2501.04269v1","created_at":"2026-07-05T09:58:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.04269","created_at":"2026-07-05T09:58:32Z"},{"alias_kind":"pith_short_12","alias_value":"2AUFXKIY5365","created_at":"2026-07-05T09:58:32Z"},{"alias_kind":"pith_short_16","alias_value":"2AUFXKIY5365MKDT","created_at":"2026-07-05T09:58:32Z"},{"alias_kind":"pith_short_8","alias_value":"2AUFXKIY","created_at":"2026-07-05T09:58:32Z"}],"graph_snapshots":[{"event_id":"sha256:7099d7c3269844e47ba041d07a79922edbb3233f9620289ffd7e953b5b6e7185","target":"graph","created_at":"2026-07-05T09:58:32Z","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/2501.04269/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In recent years, the remarkable success of deep neural networks (DNNs) in computer vision is largely due to large-scale, high-quality labeled datasets. Training directly on real-world datasets with label noise may result in overfitting. The traditional method is limited to deal with closed set label noise, where noisy training data has true class labels within the known label space. However, there are some real-world datasets containing open set label noise, which means that some samples belong to an unknown class outside the known label space. To address the open set label noise problem, we i","authors_text":"Liyan Ma, Qianxi Xia, Shihui Ying, Yang Sun, Yuandi Zhao, Zhijie Wen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-08T04:37:36Z","title":"Open set label noise learning with robust sample selection and margin-guided module"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.04269","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":"sha256:4f0c9d07a2f5ad06e3e2b362c1f2b95c191d570ecf19f5219bed5e713a6f1d49","target":"record","created_at":"2026-07-05T09:58:32Z","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":"16ae72b117e150052575c486e8b811ec64af5674d0950073163b83afc0ba094a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-08T04:37:36Z","title_canon_sha256":"17ac90d46bb9dc8e2d8ce24fbdd16761adc883a97b670c272b8f8b7786540c7c"},"schema_version":"1.0","source":{"id":"2501.04269","kind":"arxiv","version":1}},"canonical_sha256":"d0285ba918eefdd6287399bfa16fe144117acb52952cc19e4d1e1d2d6ba0e08f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d0285ba918eefdd6287399bfa16fe144117acb52952cc19e4d1e1d2d6ba0e08f","first_computed_at":"2026-07-05T09:58:32.549251Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:58:32.549251Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PP34mMd26iQxRFz5sXnWAml6yc0itleuhKAO6EeiAscwB4G7mUHQ4yK6ho/Iu+xTuN4mmYgyRh5FiLfrde8wCg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:58:32.549676Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.04269","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4f0c9d07a2f5ad06e3e2b362c1f2b95c191d570ecf19f5219bed5e713a6f1d49","sha256:7099d7c3269844e47ba041d07a79922edbb3233f9620289ffd7e953b5b6e7185"],"state_sha256":"34c72ca108a356e41857f755531f89a551f0cc7af51f18b186c97ca184f5c54a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"U9CGKX9zpGPD9vJ36Z3nkgbFBjvexP5poSKkNFws69rTeKQd8x4mubZ6HlqMmzyU8Zr2BJxged9qvLTv/Ic1BA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T04:39:09.363815Z","bundle_sha256":"a65e477a1d50eef1ef52a49f715913cea350ea40264f11787e143256f94c23c0"}}