{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:6KTFBSFQ5NC6JWBTUXLEPFKSY5","short_pith_number":"pith:6KTFBSFQ","schema_version":"1.0","canonical_sha256":"f2a650c8b0eb45e4d833a5d6479552c76b597654a70d6b2a9912d576e695d5cb","source":{"kind":"arxiv","id":"2103.15055","version":1},"attestation_state":"computed","paper":{"title":"Friends and Foes in Learning from Noisy Labels","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jianxin Wu, Yifan Ge, Yifan Zhou","submitted_at":"2021-03-28T06:05:17Z","abstract_excerpt":"Learning from examples with noisy labels has attracted increasing attention recently. But, this paper will show that the commonly used CIFAR-based datasets and the accuracy evaluation metric used in the literature are both inappropriate in this context. An alternative valid evaluation metric and new datasets are proposed in this paper to promote proper research and evaluation in this area. Then, friends and foes are identified from existing methods as technical components that are either beneficial or detrimental to deep learning from noisy labeled examples, respectively, and this paper improv"},"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":"2103.15055","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-03-28T06:05:17Z","cross_cats_sorted":[],"title_canon_sha256":"6afc86e22abf7e53572e2d158cc828b9533d81e51d6fea41fc220e56922fdf90","abstract_canon_sha256":"f2ed7135acbcb96291e9ef81d1d44825ec02f6b7c5dc61ab1044bf5b10ede65c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:27:02.148225Z","signature_b64":"iPAdK50ocOqMVzaF3Jz8mcV7M4MmWHv5wNGa3zJZMMmUx1p/QS0YE17m/T+F/PIb2DwwKyDZw7CVMMxPDMKbBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f2a650c8b0eb45e4d833a5d6479552c76b597654a70d6b2a9912d576e695d5cb","last_reissued_at":"2026-07-05T02:27:02.147817Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:27:02.147817Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Friends and Foes in Learning from Noisy Labels","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jianxin Wu, Yifan Ge, Yifan Zhou","submitted_at":"2021-03-28T06:05:17Z","abstract_excerpt":"Learning from examples with noisy labels has attracted increasing attention recently. But, this paper will show that the commonly used CIFAR-based datasets and the accuracy evaluation metric used in the literature are both inappropriate in this context. An alternative valid evaluation metric and new datasets are proposed in this paper to promote proper research and evaluation in this area. Then, friends and foes are identified from existing methods as technical components that are either beneficial or detrimental to deep learning from noisy labeled examples, respectively, and this paper improv"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.15055","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/2103.15055/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":"2103.15055","created_at":"2026-07-05T02:27:02.147882+00:00"},{"alias_kind":"arxiv_version","alias_value":"2103.15055v1","created_at":"2026-07-05T02:27:02.147882+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.15055","created_at":"2026-07-05T02:27:02.147882+00:00"},{"alias_kind":"pith_short_12","alias_value":"6KTFBSFQ5NC6","created_at":"2026-07-05T02:27:02.147882+00:00"},{"alias_kind":"pith_short_16","alias_value":"6KTFBSFQ5NC6JWBT","created_at":"2026-07-05T02:27:02.147882+00:00"},{"alias_kind":"pith_short_8","alias_value":"6KTFBSFQ","created_at":"2026-07-05T02:27:02.147882+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"2607.07610","citing_title":"Gamma-ray bursts reveal the history and faint contributors of cosmic reionization","ref_index":117,"is_internal_anchor":true},{"citing_arxiv_id":"2607.05182","citing_title":"Spectropolarimetric detection of baryonic mass loading in a transient relativistic jet: application to the black hole X-ray binary Swift J1727.8$-$1613","ref_index":259,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6KTFBSFQ5NC6JWBTUXLEPFKSY5","json":"https://pith.science/pith/6KTFBSFQ5NC6JWBTUXLEPFKSY5.json","graph_json":"https://pith.science/api/pith-number/6KTFBSFQ5NC6JWBTUXLEPFKSY5/graph.json","events_json":"https://pith.science/api/pith-number/6KTFBSFQ5NC6JWBTUXLEPFKSY5/events.json","paper":"https://pith.science/paper/6KTFBSFQ"},"agent_actions":{"view_html":"https://pith.science/pith/6KTFBSFQ5NC6JWBTUXLEPFKSY5","download_json":"https://pith.science/pith/6KTFBSFQ5NC6JWBTUXLEPFKSY5.json","view_paper":"https://pith.science/paper/6KTFBSFQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2103.15055&json=true","fetch_graph":"https://pith.science/api/pith-number/6KTFBSFQ5NC6JWBTUXLEPFKSY5/graph.json","fetch_events":"https://pith.science/api/pith-number/6KTFBSFQ5NC6JWBTUXLEPFKSY5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6KTFBSFQ5NC6JWBTUXLEPFKSY5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6KTFBSFQ5NC6JWBTUXLEPFKSY5/action/storage_attestation","attest_author":"https://pith.science/pith/6KTFBSFQ5NC6JWBTUXLEPFKSY5/action/author_attestation","sign_citation":"https://pith.science/pith/6KTFBSFQ5NC6JWBTUXLEPFKSY5/action/citation_signature","submit_replication":"https://pith.science/pith/6KTFBSFQ5NC6JWBTUXLEPFKSY5/action/replication_record"}},"created_at":"2026-07-05T02:27:02.147882+00:00","updated_at":"2026-07-05T02:27:02.147882+00:00"}