{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:YRCMDCXIVCWTQQMF3YEQUYVSPH","short_pith_number":"pith:YRCMDCXI","schema_version":"1.0","canonical_sha256":"c444c18ae8a8ad384185de090a62b279c79c75908a595f6199fb6af491538c7e","source":{"kind":"arxiv","id":"1409.7495","version":2},"attestation_state":"computed","paper":{"title":"Unsupervised Domain Adaptation by Backpropagation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"stat.ML","authors_text":"Victor Lempitsky, Yaroslav Ganin","submitted_at":"2014-09-26T08:22:21Z","abstract_excerpt":"Top-performing deep architectures are trained on massive amounts of labeled data. In the absence of labeled data for a certain task, domain adaptation often provides an attractive option given that labeled data of similar nature but from a different domain (e.g. synthetic images) are available. Here, we propose a new approach to domain adaptation in deep architectures that can be trained on large amount of labeled data from the source domain and large amount of unlabeled data from the target domain (no labeled target-domain data is necessary).\n  As the training progresses, the approach promote"},"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":"1409.7495","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-09-26T08:22:21Z","cross_cats_sorted":["cs.LG","cs.NE"],"title_canon_sha256":"3f64f407f22dda4d86223e80695157076745183f3da4459a587be59f8c8045cc","abstract_canon_sha256":"4d8f0443bc2603e2be87d2a340aeb855a1041c00b50a497c2b7e217f47dfcf65"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:26:02.546900Z","signature_b64":"gZZtBu83J+oBKWDFn7bBySYT1AeKQ7HK7hXplmjgRsu3Z+lwHcgP2j4noufnNeedZCMtEHVg7Wsv7r8OodNkAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c444c18ae8a8ad384185de090a62b279c79c75908a595f6199fb6af491538c7e","last_reissued_at":"2026-05-18T02:26:02.546538Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:26:02.546538Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unsupervised Domain Adaptation by Backpropagation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"stat.ML","authors_text":"Victor Lempitsky, Yaroslav Ganin","submitted_at":"2014-09-26T08:22:21Z","abstract_excerpt":"Top-performing deep architectures are trained on massive amounts of labeled data. In the absence of labeled data for a certain task, domain adaptation often provides an attractive option given that labeled data of similar nature but from a different domain (e.g. synthetic images) are available. Here, we propose a new approach to domain adaptation in deep architectures that can be trained on large amount of labeled data from the source domain and large amount of unlabeled data from the target domain (no labeled target-domain data is necessary).\n  As the training progresses, the approach promote"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1409.7495","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1409.7495","created_at":"2026-05-18T02:26:02.546593+00:00"},{"alias_kind":"arxiv_version","alias_value":"1409.7495v2","created_at":"2026-05-18T02:26:02.546593+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1409.7495","created_at":"2026-05-18T02:26:02.546593+00:00"},{"alias_kind":"pith_short_12","alias_value":"YRCMDCXIVCWT","created_at":"2026-05-18T12:28:57.508820+00:00"},{"alias_kind":"pith_short_16","alias_value":"YRCMDCXIVCWTQQMF","created_at":"2026-05-18T12:28:57.508820+00:00"},{"alias_kind":"pith_short_8","alias_value":"YRCMDCXI","created_at":"2026-05-18T12:28:57.508820+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":5,"sample":[{"citing_arxiv_id":"1907.03233","citing_title":"NIESR: Nuisance Invariant End-to-end Speech Recognition","ref_index":20,"is_internal_anchor":true},{"citing_arxiv_id":"1907.03389","citing_title":"Blending-target Domain Adaptation by Adversarial Meta-Adaptation Networks","ref_index":9,"is_internal_anchor":true},{"citing_arxiv_id":"1907.06347","citing_title":"Discriminative Active Learning","ref_index":4,"is_internal_anchor":true},{"citing_arxiv_id":"2605.18768","citing_title":"ClinQueryAgent: A Conversational Agent for Population Health Management","ref_index":160,"is_internal_anchor":true},{"citing_arxiv_id":"2605.11654","citing_title":"Weather-Robust Cross-View Geo-Localization via Prototype-Based Semantic Part Discovery","ref_index":19,"is_internal_anchor":true},{"citing_arxiv_id":"2605.11654","citing_title":"Weather-Robust Cross-View Geo-Localization via Prototype-Based Semantic Part Discovery","ref_index":19,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YRCMDCXIVCWTQQMF3YEQUYVSPH","json":"https://pith.science/pith/YRCMDCXIVCWTQQMF3YEQUYVSPH.json","graph_json":"https://pith.science/api/pith-number/YRCMDCXIVCWTQQMF3YEQUYVSPH/graph.json","events_json":"https://pith.science/api/pith-number/YRCMDCXIVCWTQQMF3YEQUYVSPH/events.json","paper":"https://pith.science/paper/YRCMDCXI"},"agent_actions":{"view_html":"https://pith.science/pith/YRCMDCXIVCWTQQMF3YEQUYVSPH","download_json":"https://pith.science/pith/YRCMDCXIVCWTQQMF3YEQUYVSPH.json","view_paper":"https://pith.science/paper/YRCMDCXI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1409.7495&json=true","fetch_graph":"https://pith.science/api/pith-number/YRCMDCXIVCWTQQMF3YEQUYVSPH/graph.json","fetch_events":"https://pith.science/api/pith-number/YRCMDCXIVCWTQQMF3YEQUYVSPH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YRCMDCXIVCWTQQMF3YEQUYVSPH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YRCMDCXIVCWTQQMF3YEQUYVSPH/action/storage_attestation","attest_author":"https://pith.science/pith/YRCMDCXIVCWTQQMF3YEQUYVSPH/action/author_attestation","sign_citation":"https://pith.science/pith/YRCMDCXIVCWTQQMF3YEQUYVSPH/action/citation_signature","submit_replication":"https://pith.science/pith/YRCMDCXIVCWTQQMF3YEQUYVSPH/action/replication_record"}},"created_at":"2026-05-18T02:26:02.546593+00:00","updated_at":"2026-05-18T02:26:02.546593+00:00"}