{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:NDGNU6WVAL2NZPCWFT2STQTHH7","short_pith_number":"pith:NDGNU6WV","schema_version":"1.0","canonical_sha256":"68ccda7ad502f4dcbc562cf529c2673fc0902abe7aa73b48cfcee7cd5e321ab7","source":{"kind":"arxiv","id":"2109.02986","version":3},"attestation_state":"computed","paper":{"title":"Instance-dependent Label-noise Learning under a Structural Causal Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Bo Han, Gang Niu, Kun Zhang, Mingming Gong, Tongliang Liu, Yu Yao","submitted_at":"2021-09-07T10:42:54Z","abstract_excerpt":"Label noise will degenerate the performance of deep learning algorithms because deep neural networks easily overfit label errors. Let X and Y denote the instance and clean label, respectively. When Y is a cause of X, according to which many datasets have been constructed, e.g., SVHN and CIFAR, the distributions of P(X) and P(Y|X) are entangled. This means that the unsupervised instances are helpful to learn the classifier and thus reduce the side effect of label noise. However, it remains elusive on how to exploit the causal information to handle the label noise problem. In this paper, by leve"},"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":"2109.02986","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2021-09-07T10:42:54Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"e1f50110db498d128b41fbf6bbfe27ed68b991e13552676fadd339b3bbe2711f","abstract_canon_sha256":"54be4d7face34de33beeb7cbafd88f3916064816c2c89f73e5ecc1e2cde17d04"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:28:41.029797Z","signature_b64":"0SWkeSvjQwGwAFi3yv2p5bs1xejHGetnh61mkBrHstgWR1rGP5khNieDTRcW/Hije/Zjik7C3ZfG3vlRNurACg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"68ccda7ad502f4dcbc562cf529c2673fc0902abe7aa73b48cfcee7cd5e321ab7","last_reissued_at":"2026-07-05T04:28:41.029308Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:28:41.029308Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Instance-dependent Label-noise Learning under a Structural Causal Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Bo Han, Gang Niu, Kun Zhang, Mingming Gong, Tongliang Liu, Yu Yao","submitted_at":"2021-09-07T10:42:54Z","abstract_excerpt":"Label noise will degenerate the performance of deep learning algorithms because deep neural networks easily overfit label errors. Let X and Y denote the instance and clean label, respectively. When Y is a cause of X, according to which many datasets have been constructed, e.g., SVHN and CIFAR, the distributions of P(X) and P(Y|X) are entangled. This means that the unsupervised instances are helpful to learn the classifier and thus reduce the side effect of label noise. However, it remains elusive on how to exploit the causal information to handle the label noise problem. In this paper, by leve"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.02986","kind":"arxiv","version":3},"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/2109.02986/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":"2109.02986","created_at":"2026-07-05T04:28:41.029377+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.02986v3","created_at":"2026-07-05T04:28:41.029377+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.02986","created_at":"2026-07-05T04:28:41.029377+00:00"},{"alias_kind":"pith_short_12","alias_value":"NDGNU6WVAL2N","created_at":"2026-07-05T04:28:41.029377+00:00"},{"alias_kind":"pith_short_16","alias_value":"NDGNU6WVAL2NZPCW","created_at":"2026-07-05T04:28:41.029377+00:00"},{"alias_kind":"pith_short_8","alias_value":"NDGNU6WV","created_at":"2026-07-05T04:28:41.029377+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.27913","citing_title":"Where LLM Annotators Fail: Label-Free Learning on Graphs with LLMs","ref_index":34,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NDGNU6WVAL2NZPCWFT2STQTHH7","json":"https://pith.science/pith/NDGNU6WVAL2NZPCWFT2STQTHH7.json","graph_json":"https://pith.science/api/pith-number/NDGNU6WVAL2NZPCWFT2STQTHH7/graph.json","events_json":"https://pith.science/api/pith-number/NDGNU6WVAL2NZPCWFT2STQTHH7/events.json","paper":"https://pith.science/paper/NDGNU6WV"},"agent_actions":{"view_html":"https://pith.science/pith/NDGNU6WVAL2NZPCWFT2STQTHH7","download_json":"https://pith.science/pith/NDGNU6WVAL2NZPCWFT2STQTHH7.json","view_paper":"https://pith.science/paper/NDGNU6WV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.02986&json=true","fetch_graph":"https://pith.science/api/pith-number/NDGNU6WVAL2NZPCWFT2STQTHH7/graph.json","fetch_events":"https://pith.science/api/pith-number/NDGNU6WVAL2NZPCWFT2STQTHH7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NDGNU6WVAL2NZPCWFT2STQTHH7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NDGNU6WVAL2NZPCWFT2STQTHH7/action/storage_attestation","attest_author":"https://pith.science/pith/NDGNU6WVAL2NZPCWFT2STQTHH7/action/author_attestation","sign_citation":"https://pith.science/pith/NDGNU6WVAL2NZPCWFT2STQTHH7/action/citation_signature","submit_replication":"https://pith.science/pith/NDGNU6WVAL2NZPCWFT2STQTHH7/action/replication_record"}},"created_at":"2026-07-05T04:28:41.029377+00:00","updated_at":"2026-07-05T04:28:41.029377+00:00"}