{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:DANRMDXQNABT2RHU23WZAPDDCB","short_pith_number":"pith:DANRMDXQ","canonical_record":{"source":{"id":"2606.07086","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-05T09:32:18Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"f91a34aef2bd0bab0fa96b55f0becfe9906f072105473f4a9e029d53deb04681","abstract_canon_sha256":"d1ded7e1bb7574341c37d4cd827667a45d9c38932561bb22a8f751ae5bc7671a"},"schema_version":"1.0"},"canonical_sha256":"181b160ef068033d44f4d6ed903c63107fccec72d610b34efc43a796cb1c93a0","source":{"kind":"arxiv","id":"2606.07086","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.07086","created_at":"2026-06-08T01:04:45Z"},{"alias_kind":"arxiv_version","alias_value":"2606.07086v1","created_at":"2026-06-08T01:04:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.07086","created_at":"2026-06-08T01:04:45Z"},{"alias_kind":"pith_short_12","alias_value":"DANRMDXQNABT","created_at":"2026-06-08T01:04:45Z"},{"alias_kind":"pith_short_16","alias_value":"DANRMDXQNABT2RHU","created_at":"2026-06-08T01:04:45Z"},{"alias_kind":"pith_short_8","alias_value":"DANRMDXQ","created_at":"2026-06-08T01:04:45Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:DANRMDXQNABT2RHU23WZAPDDCB","target":"record","payload":{"canonical_record":{"source":{"id":"2606.07086","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-05T09:32:18Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"f91a34aef2bd0bab0fa96b55f0becfe9906f072105473f4a9e029d53deb04681","abstract_canon_sha256":"d1ded7e1bb7574341c37d4cd827667a45d9c38932561bb22a8f751ae5bc7671a"},"schema_version":"1.0"},"canonical_sha256":"181b160ef068033d44f4d6ed903c63107fccec72d610b34efc43a796cb1c93a0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-08T01:04:45.475623Z","signature_b64":"Yfr9xw2RSQgLvbzGOKl3Y0pBCwF8q4s5XXT5AIrXSACWR17czVF7/E2oijTE63BKmaWB0bkVb17lIxpQlJkzCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"181b160ef068033d44f4d6ed903c63107fccec72d610b34efc43a796cb1c93a0","last_reissued_at":"2026-06-08T01:04:45.474804Z","signature_status":"signed_v1","first_computed_at":"2026-06-08T01:04:45.474804Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.07086","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":"2026-06-08T01:04:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hdcFn9FCOsPzsDYdb8qRSYP4Ro/VV3ySGjW7tsKzkJK7z+0cv0Y2/SCKUTrx3EDbpa5Uihvs0XWNJccs3DTSCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T19:43:16.270549Z"},"content_sha256":"70487eb0af579ce7b1d7c912b41d6fe736e81519435ce5118558ec22d1974568","schema_version":"1.0","event_id":"sha256:70487eb0af579ce7b1d7c912b41d6fe736e81519435ce5118558ec22d1974568"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:DANRMDXQNABT2RHU23WZAPDDCB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An Adaptive Data cleaning Framework for Noisy Label Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Chen-Hsuan Fang, Jung-Hua Wang, Pin-Hsuan Yu, Tsung-Wei Pan, Wei-Hsinag Chen","submitted_at":"2026-06-05T09:32:18Z","abstract_excerpt":"Deep neural networks (DNNs) excel in computer vision tasks given large annotated datasets. In real-world applications, however, labels are often corrupted by ambiguity, human error, or dynamic environments. Over-parameterized DNNs easily memorize these noisy labels during training, degrading model accuracy and generalization. Existing data-cleaning and sample-selection strategies often rely on manually specified thresholds, prior knowledge of the noise ratio, or a single metric (either learning dynamics or geometric structure), making them unstable in complex data regimes. This paper proposes "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.07086","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/2606.07086/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":"2026-06-08T01:04:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"46SXirLudZv2aVZnf2guasGKIztECCgae4PTi36tq8btvHG5gYIPuk+9Y1LUt+2azSf4dqWGE6e4/As+WD+KAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T19:43:16.270922Z"},"content_sha256":"84f12235b99a0b41aff41ba94d34199e9f68e0e30a8c7a4f211c8016d9ad638f","schema_version":"1.0","event_id":"sha256:84f12235b99a0b41aff41ba94d34199e9f68e0e30a8c7a4f211c8016d9ad638f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DANRMDXQNABT2RHU23WZAPDDCB/bundle.json","state_url":"https://pith.science/pith/DANRMDXQNABT2RHU23WZAPDDCB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DANRMDXQNABT2RHU23WZAPDDCB/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":"2026-06-29T19:43:16Z","links":{"resolver":"https://pith.science/pith/DANRMDXQNABT2RHU23WZAPDDCB","bundle":"https://pith.science/pith/DANRMDXQNABT2RHU23WZAPDDCB/bundle.json","state":"https://pith.science/pith/DANRMDXQNABT2RHU23WZAPDDCB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DANRMDXQNABT2RHU23WZAPDDCB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:DANRMDXQNABT2RHU23WZAPDDCB","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":"d1ded7e1bb7574341c37d4cd827667a45d9c38932561bb22a8f751ae5bc7671a","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-05T09:32:18Z","title_canon_sha256":"f91a34aef2bd0bab0fa96b55f0becfe9906f072105473f4a9e029d53deb04681"},"schema_version":"1.0","source":{"id":"2606.07086","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.07086","created_at":"2026-06-08T01:04:45Z"},{"alias_kind":"arxiv_version","alias_value":"2606.07086v1","created_at":"2026-06-08T01:04:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.07086","created_at":"2026-06-08T01:04:45Z"},{"alias_kind":"pith_short_12","alias_value":"DANRMDXQNABT","created_at":"2026-06-08T01:04:45Z"},{"alias_kind":"pith_short_16","alias_value":"DANRMDXQNABT2RHU","created_at":"2026-06-08T01:04:45Z"},{"alias_kind":"pith_short_8","alias_value":"DANRMDXQ","created_at":"2026-06-08T01:04:45Z"}],"graph_snapshots":[{"event_id":"sha256:84f12235b99a0b41aff41ba94d34199e9f68e0e30a8c7a4f211c8016d9ad638f","target":"graph","created_at":"2026-06-08T01:04:45Z","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/2606.07086/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep neural networks (DNNs) excel in computer vision tasks given large annotated datasets. In real-world applications, however, labels are often corrupted by ambiguity, human error, or dynamic environments. Over-parameterized DNNs easily memorize these noisy labels during training, degrading model accuracy and generalization. Existing data-cleaning and sample-selection strategies often rely on manually specified thresholds, prior knowledge of the noise ratio, or a single metric (either learning dynamics or geometric structure), making them unstable in complex data regimes. This paper proposes ","authors_text":"Chen-Hsuan Fang, Jung-Hua Wang, Pin-Hsuan Yu, Tsung-Wei Pan, Wei-Hsinag Chen","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-05T09:32:18Z","title":"An Adaptive Data cleaning Framework for Noisy Label Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.07086","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":"sha256:70487eb0af579ce7b1d7c912b41d6fe736e81519435ce5118558ec22d1974568","target":"record","created_at":"2026-06-08T01:04:45Z","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":"d1ded7e1bb7574341c37d4cd827667a45d9c38932561bb22a8f751ae5bc7671a","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-05T09:32:18Z","title_canon_sha256":"f91a34aef2bd0bab0fa96b55f0becfe9906f072105473f4a9e029d53deb04681"},"schema_version":"1.0","source":{"id":"2606.07086","kind":"arxiv","version":1}},"canonical_sha256":"181b160ef068033d44f4d6ed903c63107fccec72d610b34efc43a796cb1c93a0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"181b160ef068033d44f4d6ed903c63107fccec72d610b34efc43a796cb1c93a0","first_computed_at":"2026-06-08T01:04:45.474804Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-08T01:04:45.474804Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Yfr9xw2RSQgLvbzGOKl3Y0pBCwF8q4s5XXT5AIrXSACWR17czVF7/E2oijTE63BKmaWB0bkVb17lIxpQlJkzCg==","signature_status":"signed_v1","signed_at":"2026-06-08T01:04:45.475623Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.07086","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:70487eb0af579ce7b1d7c912b41d6fe736e81519435ce5118558ec22d1974568","sha256:84f12235b99a0b41aff41ba94d34199e9f68e0e30a8c7a4f211c8016d9ad638f"],"state_sha256":"354acd838f93ef17498e9c620780180417da3b50450a138a66f7018ed25d3cdf"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FfzJrp5I/xEDyyLnFCda5taUnwx1q+xaoJDSTDxgQ8xk/1+D2IFd2VQk6AWjsyrRwxqQkdIenAzK4D7QEOhYDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-29T19:43:16.272867Z","bundle_sha256":"8045f5e2ad4fe7a1d009e71421557ddf2b791b79452ebb8f8ea8690224a5f1b7"}}