{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:TCNI73FOWG6LXCHUKVGWY6YUJS","short_pith_number":"pith:TCNI73FO","schema_version":"1.0","canonical_sha256":"989a8fecaeb1bcbb88f4554d6c7b144ca21ad319891e2b175cb8243139b83d90","source":{"kind":"arxiv","id":"2412.05029","version":1},"attestation_state":"computed","paper":{"title":"Mixed Blessing: Class-Wise Embedding guided Instance-Dependent Partial Label Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Fuchao Yang, Hui Liu, Jianhong Cheng, Junhui Hou, Yongqiang Dong, Yuheng Jia","submitted_at":"2024-12-06T13:25:39Z","abstract_excerpt":"In partial label learning (PLL), every sample is associated with a candidate label set comprising the ground-truth label and several noisy labels. The conventional PLL assumes the noisy labels are randomly generated (instance-independent), while in practical scenarios, the noisy labels are always instance-dependent and are highly related to the sample features, leading to the instance-dependent partial label learning (IDPLL) problem. Instance-dependent noisy label is a double-edged sword. On one side, it may promote model training as the noisy labels can depict the sample to some extent. On th"},"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":"2412.05029","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-06T13:25:39Z","cross_cats_sorted":[],"title_canon_sha256":"1cb8d12ae9e18dd53b1691d8eb1c91749002dd4498db46550a167f066d9f59d6","abstract_canon_sha256":"a58ccf3bb1385bbd307ce68cc976b3c4065fb9941d4daed32402129a5be82e0c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:45:27.585589Z","signature_b64":"1SG6T6iqU99NySHCNelx5QcqhRDBmZEsNsf5xUGoaxQ9az4BJ/aXU4B5u6ZvYKwxYuxL/0GjWaj3EPTjd4BVAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"989a8fecaeb1bcbb88f4554d6c7b144ca21ad319891e2b175cb8243139b83d90","last_reissued_at":"2026-07-05T09:45:27.585115Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:45:27.585115Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Mixed Blessing: Class-Wise Embedding guided Instance-Dependent Partial Label Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Fuchao Yang, Hui Liu, Jianhong Cheng, Junhui Hou, Yongqiang Dong, Yuheng Jia","submitted_at":"2024-12-06T13:25:39Z","abstract_excerpt":"In partial label learning (PLL), every sample is associated with a candidate label set comprising the ground-truth label and several noisy labels. The conventional PLL assumes the noisy labels are randomly generated (instance-independent), while in practical scenarios, the noisy labels are always instance-dependent and are highly related to the sample features, leading to the instance-dependent partial label learning (IDPLL) problem. Instance-dependent noisy label is a double-edged sword. On one side, it may promote model training as the noisy labels can depict the sample to some extent. On th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.05029","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/2412.05029/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":"2412.05029","created_at":"2026-07-05T09:45:27.585183+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.05029v1","created_at":"2026-07-05T09:45:27.585183+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.05029","created_at":"2026-07-05T09:45:27.585183+00:00"},{"alias_kind":"pith_short_12","alias_value":"TCNI73FOWG6L","created_at":"2026-07-05T09:45:27.585183+00:00"},{"alias_kind":"pith_short_16","alias_value":"TCNI73FOWG6LXCHU","created_at":"2026-07-05T09:45:27.585183+00:00"},{"alias_kind":"pith_short_8","alias_value":"TCNI73FO","created_at":"2026-07-05T09:45:27.585183+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/TCNI73FOWG6LXCHUKVGWY6YUJS","json":"https://pith.science/pith/TCNI73FOWG6LXCHUKVGWY6YUJS.json","graph_json":"https://pith.science/api/pith-number/TCNI73FOWG6LXCHUKVGWY6YUJS/graph.json","events_json":"https://pith.science/api/pith-number/TCNI73FOWG6LXCHUKVGWY6YUJS/events.json","paper":"https://pith.science/paper/TCNI73FO"},"agent_actions":{"view_html":"https://pith.science/pith/TCNI73FOWG6LXCHUKVGWY6YUJS","download_json":"https://pith.science/pith/TCNI73FOWG6LXCHUKVGWY6YUJS.json","view_paper":"https://pith.science/paper/TCNI73FO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.05029&json=true","fetch_graph":"https://pith.science/api/pith-number/TCNI73FOWG6LXCHUKVGWY6YUJS/graph.json","fetch_events":"https://pith.science/api/pith-number/TCNI73FOWG6LXCHUKVGWY6YUJS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TCNI73FOWG6LXCHUKVGWY6YUJS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TCNI73FOWG6LXCHUKVGWY6YUJS/action/storage_attestation","attest_author":"https://pith.science/pith/TCNI73FOWG6LXCHUKVGWY6YUJS/action/author_attestation","sign_citation":"https://pith.science/pith/TCNI73FOWG6LXCHUKVGWY6YUJS/action/citation_signature","submit_replication":"https://pith.science/pith/TCNI73FOWG6LXCHUKVGWY6YUJS/action/replication_record"}},"created_at":"2026-07-05T09:45:27.585183+00:00","updated_at":"2026-07-05T09:45:27.585183+00:00"}