{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:CS4SRTC5XPAYSJCT72I5TQ2QYR","short_pith_number":"pith:CS4SRTC5","schema_version":"1.0","canonical_sha256":"14b928cc5dbbc1892453fe91d9c350c4512bbd9cf44d8da32291b926f7952c32","source":{"kind":"arxiv","id":"2111.00056","version":1},"attestation_state":"computed","paper":{"title":"Generalized Data Weighting via Class-level Gradient Manipulation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","cs.LG","math.IT"],"primary_cat":"cs.CV","authors_text":"Can Chen, Dejing Dou, Erqun Dong, Hao Liu, Shuhao Zheng, Xi Chen, Xue Liu","submitted_at":"2021-10-29T19:30:01Z","abstract_excerpt":"Label noise and class imbalance are two major issues coexisting in real-world datasets. To alleviate the two issues, state-of-the-art methods reweight each instance by leveraging a small amount of clean and unbiased data. Yet, these methods overlook class-level information within each instance, which can be further utilized to improve performance. To this end, in this paper, we propose Generalized Data Weighting (GDW) to simultaneously mitigate label noise and class imbalance by manipulating gradients at the class level. To be specific, GDW unrolls the loss gradient to class-level gradients by"},"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":"2111.00056","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-10-29T19:30:01Z","cross_cats_sorted":["cs.IT","cs.LG","math.IT"],"title_canon_sha256":"db7b26bd78bc4968202626011d4094da2c1c0f9f1cf88e26c4e306f2380bffc3","abstract_canon_sha256":"6cd2ec965146ebe6d05f69e53069a81f3812d7cdaa65d2976d3cb8c474e61f38"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:27:17.515587Z","signature_b64":"/x2wD4BAJk/zxANyGWUXR3DnR1RtVity+stNODtREt9KqcvptPcdo4VEve/apOh1Lk8RFTJ+yL/1beegSwM/Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"14b928cc5dbbc1892453fe91d9c350c4512bbd9cf44d8da32291b926f7952c32","last_reissued_at":"2026-07-05T03:27:17.515019Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:27:17.515019Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Generalized Data Weighting via Class-level Gradient Manipulation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","cs.LG","math.IT"],"primary_cat":"cs.CV","authors_text":"Can Chen, Dejing Dou, Erqun Dong, Hao Liu, Shuhao Zheng, Xi Chen, Xue Liu","submitted_at":"2021-10-29T19:30:01Z","abstract_excerpt":"Label noise and class imbalance are two major issues coexisting in real-world datasets. To alleviate the two issues, state-of-the-art methods reweight each instance by leveraging a small amount of clean and unbiased data. Yet, these methods overlook class-level information within each instance, which can be further utilized to improve performance. To this end, in this paper, we propose Generalized Data Weighting (GDW) to simultaneously mitigate label noise and class imbalance by manipulating gradients at the class level. To be specific, GDW unrolls the loss gradient to class-level gradients by"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.00056","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/2111.00056/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":"2111.00056","created_at":"2026-07-05T03:27:17.515086+00:00"},{"alias_kind":"arxiv_version","alias_value":"2111.00056v1","created_at":"2026-07-05T03:27:17.515086+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.00056","created_at":"2026-07-05T03:27:17.515086+00:00"},{"alias_kind":"pith_short_12","alias_value":"CS4SRTC5XPAY","created_at":"2026-07-05T03:27:17.515086+00:00"},{"alias_kind":"pith_short_16","alias_value":"CS4SRTC5XPAYSJCT","created_at":"2026-07-05T03:27:17.515086+00:00"},{"alias_kind":"pith_short_8","alias_value":"CS4SRTC5","created_at":"2026-07-05T03:27:17.515086+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CS4SRTC5XPAYSJCT72I5TQ2QYR","json":"https://pith.science/pith/CS4SRTC5XPAYSJCT72I5TQ2QYR.json","graph_json":"https://pith.science/api/pith-number/CS4SRTC5XPAYSJCT72I5TQ2QYR/graph.json","events_json":"https://pith.science/api/pith-number/CS4SRTC5XPAYSJCT72I5TQ2QYR/events.json","paper":"https://pith.science/paper/CS4SRTC5"},"agent_actions":{"view_html":"https://pith.science/pith/CS4SRTC5XPAYSJCT72I5TQ2QYR","download_json":"https://pith.science/pith/CS4SRTC5XPAYSJCT72I5TQ2QYR.json","view_paper":"https://pith.science/paper/CS4SRTC5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2111.00056&json=true","fetch_graph":"https://pith.science/api/pith-number/CS4SRTC5XPAYSJCT72I5TQ2QYR/graph.json","fetch_events":"https://pith.science/api/pith-number/CS4SRTC5XPAYSJCT72I5TQ2QYR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CS4SRTC5XPAYSJCT72I5TQ2QYR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CS4SRTC5XPAYSJCT72I5TQ2QYR/action/storage_attestation","attest_author":"https://pith.science/pith/CS4SRTC5XPAYSJCT72I5TQ2QYR/action/author_attestation","sign_citation":"https://pith.science/pith/CS4SRTC5XPAYSJCT72I5TQ2QYR/action/citation_signature","submit_replication":"https://pith.science/pith/CS4SRTC5XPAYSJCT72I5TQ2QYR/action/replication_record"}},"created_at":"2026-07-05T03:27:17.515086+00:00","updated_at":"2026-07-05T03:27:17.515086+00:00"}