{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:WO6VIFJ4UG7YZWHFVLE4LTTCNN","short_pith_number":"pith:WO6VIFJ4","schema_version":"1.0","canonical_sha256":"b3bd54153ca1bf8cd8e5aac9c5ce626b4bbda859d3c090b475bf60acb9d90fe3","source":{"kind":"arxiv","id":"2010.03978","version":1},"attestation_state":"computed","paper":{"title":"A Brief Review of Domain Adaptation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Abolfazl Farahani, Hamid R. Arabnia, Khaled Rasheed, Sahar Voghoei","submitted_at":"2020-10-07T07:05:32Z","abstract_excerpt":"Classical machine learning assumes that the training and test sets come from the same distributions. Therefore, a model learned from the labeled training data is expected to perform well on the test data. However, This assumption may not always hold in real-world applications where the training and the test data fall from different distributions, due to many factors, e.g., collecting the training and test sets from different sources, or having an out-dated training set due to the change of data over time. In this case, there would be a discrepancy across domain distributions, and naively apply"},"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":"2010.03978","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-07T07:05:32Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"5e2e76401fec8f81a02abe5bb632e69ede5cf04f7f9e202a4a07718d0f7cec46","abstract_canon_sha256":"f4c817cdae52d99252091782c6a6f78c706dfd58a0c5ddfab4c2f8a634d6b268"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:41:34.347944Z","signature_b64":"1DiYkjGjjKxMxHa5JhNQAxPXGQkRRQ82ht3MjIfEL9l+j8HxwFUIomA3MYf8PPdnHv3iWHJ3cMXEOMNI6ilUBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b3bd54153ca1bf8cd8e5aac9c5ce626b4bbda859d3c090b475bf60acb9d90fe3","last_reissued_at":"2026-07-05T01:41:34.347530Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:41:34.347530Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Brief Review of Domain Adaptation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Abolfazl Farahani, Hamid R. Arabnia, Khaled Rasheed, Sahar Voghoei","submitted_at":"2020-10-07T07:05:32Z","abstract_excerpt":"Classical machine learning assumes that the training and test sets come from the same distributions. Therefore, a model learned from the labeled training data is expected to perform well on the test data. However, This assumption may not always hold in real-world applications where the training and the test data fall from different distributions, due to many factors, e.g., collecting the training and test sets from different sources, or having an out-dated training set due to the change of data over time. In this case, there would be a discrepancy across domain distributions, and naively apply"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.03978","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/2010.03978/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":"2010.03978","created_at":"2026-07-05T01:41:34.347584+00:00"},{"alias_kind":"arxiv_version","alias_value":"2010.03978v1","created_at":"2026-07-05T01:41:34.347584+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.03978","created_at":"2026-07-05T01:41:34.347584+00:00"},{"alias_kind":"pith_short_12","alias_value":"WO6VIFJ4UG7Y","created_at":"2026-07-05T01:41:34.347584+00:00"},{"alias_kind":"pith_short_16","alias_value":"WO6VIFJ4UG7YZWHF","created_at":"2026-07-05T01:41:34.347584+00:00"},{"alias_kind":"pith_short_8","alias_value":"WO6VIFJ4","created_at":"2026-07-05T01:41:34.347584+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2603.05719","citing_title":"Unsupervised domain adaptation for radioisotope identification in gamma spectroscopy","ref_index":18,"is_internal_anchor":false},{"citing_arxiv_id":"2604.07409","citing_title":"GAN-based Domain Adaptation for Image-aware Layout Generation in Advertising Poster Design","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2604.05383","citing_title":"Towards Effective In-context Cross-domain Knowledge Transfer via Domain-invariant-neurons-based Retrieval","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2604.17542","citing_title":"Dual Strategies for Test-Time Adaptation","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WO6VIFJ4UG7YZWHFVLE4LTTCNN","json":"https://pith.science/pith/WO6VIFJ4UG7YZWHFVLE4LTTCNN.json","graph_json":"https://pith.science/api/pith-number/WO6VIFJ4UG7YZWHFVLE4LTTCNN/graph.json","events_json":"https://pith.science/api/pith-number/WO6VIFJ4UG7YZWHFVLE4LTTCNN/events.json","paper":"https://pith.science/paper/WO6VIFJ4"},"agent_actions":{"view_html":"https://pith.science/pith/WO6VIFJ4UG7YZWHFVLE4LTTCNN","download_json":"https://pith.science/pith/WO6VIFJ4UG7YZWHFVLE4LTTCNN.json","view_paper":"https://pith.science/paper/WO6VIFJ4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2010.03978&json=true","fetch_graph":"https://pith.science/api/pith-number/WO6VIFJ4UG7YZWHFVLE4LTTCNN/graph.json","fetch_events":"https://pith.science/api/pith-number/WO6VIFJ4UG7YZWHFVLE4LTTCNN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WO6VIFJ4UG7YZWHFVLE4LTTCNN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WO6VIFJ4UG7YZWHFVLE4LTTCNN/action/storage_attestation","attest_author":"https://pith.science/pith/WO6VIFJ4UG7YZWHFVLE4LTTCNN/action/author_attestation","sign_citation":"https://pith.science/pith/WO6VIFJ4UG7YZWHFVLE4LTTCNN/action/citation_signature","submit_replication":"https://pith.science/pith/WO6VIFJ4UG7YZWHFVLE4LTTCNN/action/replication_record"}},"created_at":"2026-07-05T01:41:34.347584+00:00","updated_at":"2026-07-05T01:41:34.347584+00:00"}