{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:FLLNQVV2A32NWW27XTXZMRHTPL","short_pith_number":"pith:FLLNQVV2","schema_version":"1.0","canonical_sha256":"2ad6d856ba06f4db5b5fbcef9644f37af8e0804ac1424062761d8498c9bc1169","source":{"kind":"arxiv","id":"2503.23712","version":1},"attestation_state":"computed","paper":{"title":"ElimPCL: Eliminating Noise Accumulation with Progressive Curriculum Labeling for Source-Free Domain Adaptation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hao Wu, Hao Zheng, Jian Zhang, Jie Cheng, Lei Wang, Meiguang Zheng","submitted_at":"2025-03-31T04:28:27Z","abstract_excerpt":"Source-Free Domain Adaptation (SFDA) aims to train a target model without source data, and the key is to generate pseudo-labels using a pre-trained source model. However, we observe that the source model often produces highly uncertain pseudo-labels for hard samples, particularly those heavily affected by domain shifts, leading to these noisy pseudo-labels being introduced even before adaptation and further reinforced through parameter updates. Additionally, they continuously influence neighbor samples through propagation in the feature space.To eliminate the issue of noise accumulation, we pr"},"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":"2503.23712","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-03-31T04:28:27Z","cross_cats_sorted":[],"title_canon_sha256":"6a2996bbe1ce619da6c55cec545fc3bc67b52d6240f2fefde44565510305e5bf","abstract_canon_sha256":"baa0ee416a3b1d85c5b5d1b74240f4cc5e8246717ca9e3c41d81028f97d907a1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:41:58.305154Z","signature_b64":"3D26GRxkarobhCSUCiXPOZDl1N8gqVHLn9PMHgARXBwlsSBE4rIW6MFIM1ztx3k3RVD+VoT8AXHY/RvO/9WVCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2ad6d856ba06f4db5b5fbcef9644f37af8e0804ac1424062761d8498c9bc1169","last_reissued_at":"2026-07-05T10:41:58.304668Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:41:58.304668Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ElimPCL: Eliminating Noise Accumulation with Progressive Curriculum Labeling for Source-Free Domain Adaptation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hao Wu, Hao Zheng, Jian Zhang, Jie Cheng, Lei Wang, Meiguang Zheng","submitted_at":"2025-03-31T04:28:27Z","abstract_excerpt":"Source-Free Domain Adaptation (SFDA) aims to train a target model without source data, and the key is to generate pseudo-labels using a pre-trained source model. However, we observe that the source model often produces highly uncertain pseudo-labels for hard samples, particularly those heavily affected by domain shifts, leading to these noisy pseudo-labels being introduced even before adaptation and further reinforced through parameter updates. Additionally, they continuously influence neighbor samples through propagation in the feature space.To eliminate the issue of noise accumulation, we pr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.23712","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/2503.23712/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":"2503.23712","created_at":"2026-07-05T10:41:58.304725+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.23712v1","created_at":"2026-07-05T10:41:58.304725+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.23712","created_at":"2026-07-05T10:41:58.304725+00:00"},{"alias_kind":"pith_short_12","alias_value":"FLLNQVV2A32N","created_at":"2026-07-05T10:41:58.304725+00:00"},{"alias_kind":"pith_short_16","alias_value":"FLLNQVV2A32NWW27","created_at":"2026-07-05T10:41:58.304725+00:00"},{"alias_kind":"pith_short_8","alias_value":"FLLNQVV2","created_at":"2026-07-05T10:41:58.304725+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/FLLNQVV2A32NWW27XTXZMRHTPL","json":"https://pith.science/pith/FLLNQVV2A32NWW27XTXZMRHTPL.json","graph_json":"https://pith.science/api/pith-number/FLLNQVV2A32NWW27XTXZMRHTPL/graph.json","events_json":"https://pith.science/api/pith-number/FLLNQVV2A32NWW27XTXZMRHTPL/events.json","paper":"https://pith.science/paper/FLLNQVV2"},"agent_actions":{"view_html":"https://pith.science/pith/FLLNQVV2A32NWW27XTXZMRHTPL","download_json":"https://pith.science/pith/FLLNQVV2A32NWW27XTXZMRHTPL.json","view_paper":"https://pith.science/paper/FLLNQVV2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.23712&json=true","fetch_graph":"https://pith.science/api/pith-number/FLLNQVV2A32NWW27XTXZMRHTPL/graph.json","fetch_events":"https://pith.science/api/pith-number/FLLNQVV2A32NWW27XTXZMRHTPL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FLLNQVV2A32NWW27XTXZMRHTPL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FLLNQVV2A32NWW27XTXZMRHTPL/action/storage_attestation","attest_author":"https://pith.science/pith/FLLNQVV2A32NWW27XTXZMRHTPL/action/author_attestation","sign_citation":"https://pith.science/pith/FLLNQVV2A32NWW27XTXZMRHTPL/action/citation_signature","submit_replication":"https://pith.science/pith/FLLNQVV2A32NWW27XTXZMRHTPL/action/replication_record"}},"created_at":"2026-07-05T10:41:58.304725+00:00","updated_at":"2026-07-05T10:41:58.304725+00:00"}