{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:BYMCUOTR7U54KIJYSMMLUZ7RUR","short_pith_number":"pith:BYMCUOTR","schema_version":"1.0","canonical_sha256":"0e182a3a71fd3bc521389318ba67f1a474c131e409ca9759f9e7d783c8bfd825","source":{"kind":"arxiv","id":"2203.07375","version":1},"attestation_state":"computed","paper":{"title":"From Big to Small: Adaptive Learning to Partial-Set Domains","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jianmin Wang, Kaichao You, Mingsheng Long, Zhangjie Cao, Ziyang Zhang","submitted_at":"2022-03-14T07:02:45Z","abstract_excerpt":"Domain adaptation targets at knowledge acquisition and dissemination from a labeled source domain to an unlabeled target domain under distribution shift. Still, the common requirement of identical class space shared across domains hinders applications of domain adaptation to partial-set domains. Recent advances show that deep pre-trained models of large scale endow rich knowledge to tackle diverse downstream tasks of small scale. Thus, there is a strong incentive to adapt models from large-scale domains to small-scale domains. This paper introduces Partial Domain Adaptation (PDA), a learning p"},"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":"2203.07375","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-03-14T07:02:45Z","cross_cats_sorted":[],"title_canon_sha256":"f7ca70cc19c467c2cf3f9491bb6982cd99dc526616de22dd41b28d82daa993bc","abstract_canon_sha256":"903367b0c22b7a321b7c7df4483c4865753dea655b21a57327e89ca977e847e2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:05:04.062830Z","signature_b64":"Oh6iMF1VuyeIEAt9GsWDeMCdbgK9tgFza4paBfPkb+oK8UN1AqgSBtr/IKuTLIgtdEFSDGXUwks6msAh/JS1Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0e182a3a71fd3bc521389318ba67f1a474c131e409ca9759f9e7d783c8bfd825","last_reissued_at":"2026-07-05T04:05:04.062406Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:05:04.062406Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"From Big to Small: Adaptive Learning to Partial-Set Domains","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jianmin Wang, Kaichao You, Mingsheng Long, Zhangjie Cao, Ziyang Zhang","submitted_at":"2022-03-14T07:02:45Z","abstract_excerpt":"Domain adaptation targets at knowledge acquisition and dissemination from a labeled source domain to an unlabeled target domain under distribution shift. Still, the common requirement of identical class space shared across domains hinders applications of domain adaptation to partial-set domains. Recent advances show that deep pre-trained models of large scale endow rich knowledge to tackle diverse downstream tasks of small scale. Thus, there is a strong incentive to adapt models from large-scale domains to small-scale domains. This paper introduces Partial Domain Adaptation (PDA), a learning p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.07375","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/2203.07375/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":"2203.07375","created_at":"2026-07-05T04:05:04.062462+00:00"},{"alias_kind":"arxiv_version","alias_value":"2203.07375v1","created_at":"2026-07-05T04:05:04.062462+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.07375","created_at":"2026-07-05T04:05:04.062462+00:00"},{"alias_kind":"pith_short_12","alias_value":"BYMCUOTR7U54","created_at":"2026-07-05T04:05:04.062462+00:00"},{"alias_kind":"pith_short_16","alias_value":"BYMCUOTR7U54KIJY","created_at":"2026-07-05T04:05:04.062462+00:00"},{"alias_kind":"pith_short_8","alias_value":"BYMCUOTR","created_at":"2026-07-05T04:05:04.062462+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/BYMCUOTR7U54KIJYSMMLUZ7RUR","json":"https://pith.science/pith/BYMCUOTR7U54KIJYSMMLUZ7RUR.json","graph_json":"https://pith.science/api/pith-number/BYMCUOTR7U54KIJYSMMLUZ7RUR/graph.json","events_json":"https://pith.science/api/pith-number/BYMCUOTR7U54KIJYSMMLUZ7RUR/events.json","paper":"https://pith.science/paper/BYMCUOTR"},"agent_actions":{"view_html":"https://pith.science/pith/BYMCUOTR7U54KIJYSMMLUZ7RUR","download_json":"https://pith.science/pith/BYMCUOTR7U54KIJYSMMLUZ7RUR.json","view_paper":"https://pith.science/paper/BYMCUOTR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2203.07375&json=true","fetch_graph":"https://pith.science/api/pith-number/BYMCUOTR7U54KIJYSMMLUZ7RUR/graph.json","fetch_events":"https://pith.science/api/pith-number/BYMCUOTR7U54KIJYSMMLUZ7RUR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BYMCUOTR7U54KIJYSMMLUZ7RUR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BYMCUOTR7U54KIJYSMMLUZ7RUR/action/storage_attestation","attest_author":"https://pith.science/pith/BYMCUOTR7U54KIJYSMMLUZ7RUR/action/author_attestation","sign_citation":"https://pith.science/pith/BYMCUOTR7U54KIJYSMMLUZ7RUR/action/citation_signature","submit_replication":"https://pith.science/pith/BYMCUOTR7U54KIJYSMMLUZ7RUR/action/replication_record"}},"created_at":"2026-07-05T04:05:04.062462+00:00","updated_at":"2026-07-05T04:05:04.062462+00:00"}