{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:7VJEN7KSDXGHZ5PRYIUPBAOE2U","short_pith_number":"pith:7VJEN7KS","schema_version":"1.0","canonical_sha256":"fd5246fd521dcc7cf5f1c228f081c4d52a8cccfb06bd4d5aa9a2be9da802c970","source":{"kind":"arxiv","id":"2306.04160","version":1},"attestation_state":"computed","paper":{"title":"Rethinking Weak Supervision in Helping Contrastive Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jingyi Cui, Weiran Huang, Yifei Wang, Yisen Wang","submitted_at":"2023-06-07T05:18:27Z","abstract_excerpt":"Contrastive learning has shown outstanding performances in both supervised and unsupervised learning, and has recently been introduced to solve weakly supervised learning problems such as semi-supervised learning and noisy label learning. Despite the empirical evidence showing that semi-supervised labels improve the representations of contrastive learning, it remains unknown if noisy supervised information can be directly used in training instead of after manual denoising. Therefore, to explore the mechanical differences between semi-supervised and noisy-labeled information in helping contrast"},"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":"2306.04160","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-07T05:18:27Z","cross_cats_sorted":[],"title_canon_sha256":"cb99feae8b0003a4511ad9e4c37be43d88b7c84642daaa5e358ad4db0c8eb61a","abstract_canon_sha256":"1cdf0cd73b21ca2866708eedab41ba5f3395e0901780488cb79c5d489daf774f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:18:26.600497Z","signature_b64":"ssJ7nkW41/xf2TUIsB68vTcRCa5loxCdGtRzLanLJT/HskcPKhwcKbCFLn+i+JFc1+vMTQDEDARW11zztvnHAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fd5246fd521dcc7cf5f1c228f081c4d52a8cccfb06bd4d5aa9a2be9da802c970","last_reissued_at":"2026-07-05T06:18:26.600069Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:18:26.600069Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Rethinking Weak Supervision in Helping Contrastive Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jingyi Cui, Weiran Huang, Yifei Wang, Yisen Wang","submitted_at":"2023-06-07T05:18:27Z","abstract_excerpt":"Contrastive learning has shown outstanding performances in both supervised and unsupervised learning, and has recently been introduced to solve weakly supervised learning problems such as semi-supervised learning and noisy label learning. Despite the empirical evidence showing that semi-supervised labels improve the representations of contrastive learning, it remains unknown if noisy supervised information can be directly used in training instead of after manual denoising. Therefore, to explore the mechanical differences between semi-supervised and noisy-labeled information in helping contrast"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.04160","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/2306.04160/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":"2306.04160","created_at":"2026-07-05T06:18:26.600125+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.04160v1","created_at":"2026-07-05T06:18:26.600125+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.04160","created_at":"2026-07-05T06:18:26.600125+00:00"},{"alias_kind":"pith_short_12","alias_value":"7VJEN7KSDXGH","created_at":"2026-07-05T06:18:26.600125+00:00"},{"alias_kind":"pith_short_16","alias_value":"7VJEN7KSDXGHZ5PR","created_at":"2026-07-05T06:18:26.600125+00:00"},{"alias_kind":"pith_short_8","alias_value":"7VJEN7KS","created_at":"2026-07-05T06:18:26.600125+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.02451","citing_title":"Weak Supervision for Real World Graphs","ref_index":37,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7VJEN7KSDXGHZ5PRYIUPBAOE2U","json":"https://pith.science/pith/7VJEN7KSDXGHZ5PRYIUPBAOE2U.json","graph_json":"https://pith.science/api/pith-number/7VJEN7KSDXGHZ5PRYIUPBAOE2U/graph.json","events_json":"https://pith.science/api/pith-number/7VJEN7KSDXGHZ5PRYIUPBAOE2U/events.json","paper":"https://pith.science/paper/7VJEN7KS"},"agent_actions":{"view_html":"https://pith.science/pith/7VJEN7KSDXGHZ5PRYIUPBAOE2U","download_json":"https://pith.science/pith/7VJEN7KSDXGHZ5PRYIUPBAOE2U.json","view_paper":"https://pith.science/paper/7VJEN7KS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.04160&json=true","fetch_graph":"https://pith.science/api/pith-number/7VJEN7KSDXGHZ5PRYIUPBAOE2U/graph.json","fetch_events":"https://pith.science/api/pith-number/7VJEN7KSDXGHZ5PRYIUPBAOE2U/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7VJEN7KSDXGHZ5PRYIUPBAOE2U/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7VJEN7KSDXGHZ5PRYIUPBAOE2U/action/storage_attestation","attest_author":"https://pith.science/pith/7VJEN7KSDXGHZ5PRYIUPBAOE2U/action/author_attestation","sign_citation":"https://pith.science/pith/7VJEN7KSDXGHZ5PRYIUPBAOE2U/action/citation_signature","submit_replication":"https://pith.science/pith/7VJEN7KSDXGHZ5PRYIUPBAOE2U/action/replication_record"}},"created_at":"2026-07-05T06:18:26.600125+00:00","updated_at":"2026-07-05T06:18:26.600125+00:00"}