{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:VYFHWW3WPBIK5VJFPMTETRZDXU","short_pith_number":"pith:VYFHWW3W","schema_version":"1.0","canonical_sha256":"ae0a7b5b767850aed5257b2649c723bd0ba1a7c0f99f130ebe91327f0cc35ee8","source":{"kind":"arxiv","id":"2206.11492","version":4},"attestation_state":"computed","paper":{"title":"Gradual Domain Adaptation via Normalizing Flows","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Hideitsu Hino, Shogo Sagawa","submitted_at":"2022-06-23T06:24:50Z","abstract_excerpt":"Standard domain adaptation methods do not work well when a large gap exists between the source and target domains. Gradual domain adaptation is one of the approaches used to address the problem. It involves leveraging the intermediate domain, which gradually shifts from the source domain to the target domain. In previous work, it is assumed that the number of intermediate domains is large and the distance between adjacent domains is small; hence, the gradual domain adaptation algorithm, involving self-training with unlabeled datasets, is applicable. In practice, however, gradual self-training "},"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":"2206.11492","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2022-06-23T06:24:50Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"10103d12e9f0c995fdf702d9d3e9125edbfcda2b85f601e1581a14ddbabf3f31","abstract_canon_sha256":"ffed2ff8b4030af8120c0bd272f2fc194acfebb3c3db045c459758c2ebd9684c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:36:19.010039Z","signature_b64":"a630BFemLARybatbAA0B5D61VAafd5zJ1NNsjRMZRPpUcJB0ziG1afyRyUNDWQ9vTWb/1QV9cqHji6zdHKeXCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ae0a7b5b767850aed5257b2649c723bd0ba1a7c0f99f130ebe91327f0cc35ee8","last_reissued_at":"2026-07-05T07:36:19.009571Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:36:19.009571Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Gradual Domain Adaptation via Normalizing Flows","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Hideitsu Hino, Shogo Sagawa","submitted_at":"2022-06-23T06:24:50Z","abstract_excerpt":"Standard domain adaptation methods do not work well when a large gap exists between the source and target domains. Gradual domain adaptation is one of the approaches used to address the problem. It involves leveraging the intermediate domain, which gradually shifts from the source domain to the target domain. In previous work, it is assumed that the number of intermediate domains is large and the distance between adjacent domains is small; hence, the gradual domain adaptation algorithm, involving self-training with unlabeled datasets, is applicable. In practice, however, gradual self-training "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.11492","kind":"arxiv","version":4},"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/2206.11492/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":"2206.11492","created_at":"2026-07-05T07:36:19.009627+00:00"},{"alias_kind":"arxiv_version","alias_value":"2206.11492v4","created_at":"2026-07-05T07:36:19.009627+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.11492","created_at":"2026-07-05T07:36:19.009627+00:00"},{"alias_kind":"pith_short_12","alias_value":"VYFHWW3WPBIK","created_at":"2026-07-05T07:36:19.009627+00:00"},{"alias_kind":"pith_short_16","alias_value":"VYFHWW3WPBIK5VJF","created_at":"2026-07-05T07:36:19.009627+00:00"},{"alias_kind":"pith_short_8","alias_value":"VYFHWW3W","created_at":"2026-07-05T07:36:19.009627+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2501.17443","citing_title":"Gradual Domain Adaptation for Graph Learning","ref_index":47,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VYFHWW3WPBIK5VJFPMTETRZDXU","json":"https://pith.science/pith/VYFHWW3WPBIK5VJFPMTETRZDXU.json","graph_json":"https://pith.science/api/pith-number/VYFHWW3WPBIK5VJFPMTETRZDXU/graph.json","events_json":"https://pith.science/api/pith-number/VYFHWW3WPBIK5VJFPMTETRZDXU/events.json","paper":"https://pith.science/paper/VYFHWW3W"},"agent_actions":{"view_html":"https://pith.science/pith/VYFHWW3WPBIK5VJFPMTETRZDXU","download_json":"https://pith.science/pith/VYFHWW3WPBIK5VJFPMTETRZDXU.json","view_paper":"https://pith.science/paper/VYFHWW3W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2206.11492&json=true","fetch_graph":"https://pith.science/api/pith-number/VYFHWW3WPBIK5VJFPMTETRZDXU/graph.json","fetch_events":"https://pith.science/api/pith-number/VYFHWW3WPBIK5VJFPMTETRZDXU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VYFHWW3WPBIK5VJFPMTETRZDXU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VYFHWW3WPBIK5VJFPMTETRZDXU/action/storage_attestation","attest_author":"https://pith.science/pith/VYFHWW3WPBIK5VJFPMTETRZDXU/action/author_attestation","sign_citation":"https://pith.science/pith/VYFHWW3WPBIK5VJFPMTETRZDXU/action/citation_signature","submit_replication":"https://pith.science/pith/VYFHWW3WPBIK5VJFPMTETRZDXU/action/replication_record"}},"created_at":"2026-07-05T07:36:19.009627+00:00","updated_at":"2026-07-05T07:36:19.009627+00:00"}