{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:HXDK2VMVGHI2ZBQ6CHEVNLVRBQ","short_pith_number":"pith:HXDK2VMV","schema_version":"1.0","canonical_sha256":"3dc6ad559531d1ac861e11c956aeb10c0f742ae61c979fe82c1a699552c12b23","source":{"kind":"arxiv","id":"2112.06161","version":1},"attestation_state":"computed","paper":{"title":"Semi-supervised Domain Adaptive Structure Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Can Qin, Huan Wang, Lichen Wang, Qianqian Ma, Yun Fu, Yu Yin","submitted_at":"2021-12-12T06:11:16Z","abstract_excerpt":"Semi-supervised domain adaptation (SSDA) is quite a challenging problem requiring methods to overcome both 1) overfitting towards poorly annotated data and 2) distribution shift across domains. Unfortunately, a simple combination of domain adaptation (DA) and semi-supervised learning (SSL) methods often fail to address such two objects because of training data bias towards labeled samples. In this paper, we introduce an adaptive structure learning method to regularize the cooperation of SSL and DA. Inspired by the multi-views learning, our proposed framework is composed of a shared feature enc"},"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":"2112.06161","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-12-12T06:11:16Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"1f160c9a0789e579ba58dd3c1bb57685cef9700a3a685306c24c5bff7fc54ea7","abstract_canon_sha256":"46368ab55968d17b6ddbe0c504ae73b5986827d62957e4268d45fbcd71810624"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:22:42.384233Z","signature_b64":"Sqtdlv4NLoqnZruBX2FvVp1AJCiK9+OyD9twIgBK45OMcLTI6oq0sWjNWK0uLAyw12f+Z6RNALR7EyvL574TAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3dc6ad559531d1ac861e11c956aeb10c0f742ae61c979fe82c1a699552c12b23","last_reissued_at":"2026-07-05T05:22:42.383817Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:22:42.383817Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Semi-supervised Domain Adaptive Structure Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Can Qin, Huan Wang, Lichen Wang, Qianqian Ma, Yun Fu, Yu Yin","submitted_at":"2021-12-12T06:11:16Z","abstract_excerpt":"Semi-supervised domain adaptation (SSDA) is quite a challenging problem requiring methods to overcome both 1) overfitting towards poorly annotated data and 2) distribution shift across domains. Unfortunately, a simple combination of domain adaptation (DA) and semi-supervised learning (SSL) methods often fail to address such two objects because of training data bias towards labeled samples. In this paper, we introduce an adaptive structure learning method to regularize the cooperation of SSL and DA. Inspired by the multi-views learning, our proposed framework is composed of a shared feature enc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.06161","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/2112.06161/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":"2112.06161","created_at":"2026-07-05T05:22:42.383875+00:00"},{"alias_kind":"arxiv_version","alias_value":"2112.06161v1","created_at":"2026-07-05T05:22:42.383875+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.06161","created_at":"2026-07-05T05:22:42.383875+00:00"},{"alias_kind":"pith_short_12","alias_value":"HXDK2VMVGHI2","created_at":"2026-07-05T05:22:42.383875+00:00"},{"alias_kind":"pith_short_16","alias_value":"HXDK2VMVGHI2ZBQ6","created_at":"2026-07-05T05:22:42.383875+00:00"},{"alias_kind":"pith_short_8","alias_value":"HXDK2VMV","created_at":"2026-07-05T05:22:42.383875+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/HXDK2VMVGHI2ZBQ6CHEVNLVRBQ","json":"https://pith.science/pith/HXDK2VMVGHI2ZBQ6CHEVNLVRBQ.json","graph_json":"https://pith.science/api/pith-number/HXDK2VMVGHI2ZBQ6CHEVNLVRBQ/graph.json","events_json":"https://pith.science/api/pith-number/HXDK2VMVGHI2ZBQ6CHEVNLVRBQ/events.json","paper":"https://pith.science/paper/HXDK2VMV"},"agent_actions":{"view_html":"https://pith.science/pith/HXDK2VMVGHI2ZBQ6CHEVNLVRBQ","download_json":"https://pith.science/pith/HXDK2VMVGHI2ZBQ6CHEVNLVRBQ.json","view_paper":"https://pith.science/paper/HXDK2VMV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2112.06161&json=true","fetch_graph":"https://pith.science/api/pith-number/HXDK2VMVGHI2ZBQ6CHEVNLVRBQ/graph.json","fetch_events":"https://pith.science/api/pith-number/HXDK2VMVGHI2ZBQ6CHEVNLVRBQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HXDK2VMVGHI2ZBQ6CHEVNLVRBQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HXDK2VMVGHI2ZBQ6CHEVNLVRBQ/action/storage_attestation","attest_author":"https://pith.science/pith/HXDK2VMVGHI2ZBQ6CHEVNLVRBQ/action/author_attestation","sign_citation":"https://pith.science/pith/HXDK2VMVGHI2ZBQ6CHEVNLVRBQ/action/citation_signature","submit_replication":"https://pith.science/pith/HXDK2VMVGHI2ZBQ6CHEVNLVRBQ/action/replication_record"}},"created_at":"2026-07-05T05:22:42.383875+00:00","updated_at":"2026-07-05T05:22:42.383875+00:00"}