{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:MLIH5VMISPZWBLKDJXQZEKOB2K","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"e3afc6940a65c21637c37f3a4f5f2eaeced4670d10cb81d29089d297abdc172c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-07-07T08:19:40Z","title_canon_sha256":"ba5b8f949f813f57c6192f9531e08c9de58ef55965020bd7b368a22c75dc086c"},"schema_version":"1.0","source":{"id":"2307.03449","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.03449","created_at":"2026-07-05T07:08:42Z"},{"alias_kind":"arxiv_version","alias_value":"2307.03449v2","created_at":"2026-07-05T07:08:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.03449","created_at":"2026-07-05T07:08:42Z"},{"alias_kind":"pith_short_12","alias_value":"MLIH5VMISPZW","created_at":"2026-07-05T07:08:42Z"},{"alias_kind":"pith_short_16","alias_value":"MLIH5VMISPZWBLKD","created_at":"2026-07-05T07:08:42Z"},{"alias_kind":"pith_short_8","alias_value":"MLIH5VMI","created_at":"2026-07-05T07:08:42Z"}],"graph_snapshots":[{"event_id":"sha256:8ba5a20b7d047f94b64f38f1dc84fc1c07ce21f81f74aa92df691fcb54596754","target":"graph","created_at":"2026-07-05T07:08:42Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2307.03449/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we introduce a realistic and challenging domain adaptation problem called Universal Semi-supervised Model Adaptation (USMA), which i) requires only a pre-trained source model, ii) allows the source and target domain to have different label sets, i.e., they share a common label set and hold their own private label set, and iii) requires only a few labeled samples in each class of the target domain. To address USMA, we propose a collaborative consistency training framework that regularizes the prediction consistency between two models, i.e., a pre-trained source model and its vari","authors_text":"Guanbin Li, Shuguang Cui, Xiaoguang Han, Yipeng Qin, Yushuang Wu, Zizheng Yan","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-07-07T08:19:40Z","title":"Universal Semi-supervised Model Adaptation via Collaborative Consistency Training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.03449","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:9d988802a65322baa337f2777e73452295d31ab34c1629bc537d43f017b2d64e","target":"record","created_at":"2026-07-05T07:08:42Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"e3afc6940a65c21637c37f3a4f5f2eaeced4670d10cb81d29089d297abdc172c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-07-07T08:19:40Z","title_canon_sha256":"ba5b8f949f813f57c6192f9531e08c9de58ef55965020bd7b368a22c75dc086c"},"schema_version":"1.0","source":{"id":"2307.03449","kind":"arxiv","version":2}},"canonical_sha256":"62d07ed58893f360ad434de19229c1d2a0ba057cad1f1dbb65bfe3f87ae439d0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"62d07ed58893f360ad434de19229c1d2a0ba057cad1f1dbb65bfe3f87ae439d0","first_computed_at":"2026-07-05T07:08:42.499582Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:08:42.499582Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qsDcVdekYWmaMh2HmVUIsN4X24abzxTNKk9IfZvBmNO5F7ckdFv97FVQzTWhPZNqJB+RSMLvLVTwGHBo6Fd8AQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:08:42.500125Z","signed_message":"canonical_sha256_bytes"},"source_id":"2307.03449","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9d988802a65322baa337f2777e73452295d31ab34c1629bc537d43f017b2d64e","sha256:8ba5a20b7d047f94b64f38f1dc84fc1c07ce21f81f74aa92df691fcb54596754"],"state_sha256":"d5f6362b2fdb570626bd9c114032feb4751fdaa3619ccb01b4fa8ddf565286df"}