{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:N5GGWKVONQVCZUQZIXMMGWPTVJ","short_pith_number":"pith:N5GGWKVO","schema_version":"1.0","canonical_sha256":"6f4c6b2aae6c2a2cd21945d8c359f3aa6714137e8206a7898836f37361752473","source":{"kind":"arxiv","id":"1808.06206","version":1},"attestation_state":"computed","paper":{"title":"TLR: Transfer Latent Representation for Unsupervised Domain Adaptation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Bo Du, Jia Wu, Lefei Zhang, Pan Xiao, Ruimin Hu, Xuelong Li","submitted_at":"2018-08-19T13:14:01Z","abstract_excerpt":"Domain adaptation refers to the process of learning prediction models in a target domain by making use of data from a source domain. Many classic methods solve the domain adaptation problem by establishing a common latent space, which may cause the loss of many important properties across both domains. In this manuscript, we develop a novel method, transfer latent representation (TLR), to learn a better latent space. Specifically, we design an objective function based on a simple linear autoencoder to derive the latent representations of both domains. The encoder in the autoencoder aims to pro"},"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":"1808.06206","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-08-19T13:14:01Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"ccdf8ab3d6a0d7369c4d0548c35fba8eacafb1464c6840e305884986390b5189","abstract_canon_sha256":"10d002544dda34f96d4949a98cd95d9c92e2e70cd22ceeda29831cd6d64ffe7f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:07:45.742869Z","signature_b64":"cHtNrqxSSJv97hKF1yiEO9fBkA4O++ScmdRE46aQGk31PWfM890tYg29piLNQZ46ela4bqhqwXTXhK7n+xNHAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6f4c6b2aae6c2a2cd21945d8c359f3aa6714137e8206a7898836f37361752473","last_reissued_at":"2026-05-18T00:07:45.742402Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:07:45.742402Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TLR: Transfer Latent Representation for Unsupervised Domain Adaptation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Bo Du, Jia Wu, Lefei Zhang, Pan Xiao, Ruimin Hu, Xuelong Li","submitted_at":"2018-08-19T13:14:01Z","abstract_excerpt":"Domain adaptation refers to the process of learning prediction models in a target domain by making use of data from a source domain. Many classic methods solve the domain adaptation problem by establishing a common latent space, which may cause the loss of many important properties across both domains. In this manuscript, we develop a novel method, transfer latent representation (TLR), to learn a better latent space. Specifically, we design an objective function based on a simple linear autoencoder to derive the latent representations of both domains. The encoder in the autoencoder aims to pro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.06206","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":""},"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":"1808.06206","created_at":"2026-05-18T00:07:45.742486+00:00"},{"alias_kind":"arxiv_version","alias_value":"1808.06206v1","created_at":"2026-05-18T00:07:45.742486+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.06206","created_at":"2026-05-18T00:07:45.742486+00:00"},{"alias_kind":"pith_short_12","alias_value":"N5GGWKVONQVC","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_16","alias_value":"N5GGWKVONQVCZUQZ","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_8","alias_value":"N5GGWKVO","created_at":"2026-05-18T12:32:40.477152+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/N5GGWKVONQVCZUQZIXMMGWPTVJ","json":"https://pith.science/pith/N5GGWKVONQVCZUQZIXMMGWPTVJ.json","graph_json":"https://pith.science/api/pith-number/N5GGWKVONQVCZUQZIXMMGWPTVJ/graph.json","events_json":"https://pith.science/api/pith-number/N5GGWKVONQVCZUQZIXMMGWPTVJ/events.json","paper":"https://pith.science/paper/N5GGWKVO"},"agent_actions":{"view_html":"https://pith.science/pith/N5GGWKVONQVCZUQZIXMMGWPTVJ","download_json":"https://pith.science/pith/N5GGWKVONQVCZUQZIXMMGWPTVJ.json","view_paper":"https://pith.science/paper/N5GGWKVO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1808.06206&json=true","fetch_graph":"https://pith.science/api/pith-number/N5GGWKVONQVCZUQZIXMMGWPTVJ/graph.json","fetch_events":"https://pith.science/api/pith-number/N5GGWKVONQVCZUQZIXMMGWPTVJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/N5GGWKVONQVCZUQZIXMMGWPTVJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/N5GGWKVONQVCZUQZIXMMGWPTVJ/action/storage_attestation","attest_author":"https://pith.science/pith/N5GGWKVONQVCZUQZIXMMGWPTVJ/action/author_attestation","sign_citation":"https://pith.science/pith/N5GGWKVONQVCZUQZIXMMGWPTVJ/action/citation_signature","submit_replication":"https://pith.science/pith/N5GGWKVONQVCZUQZIXMMGWPTVJ/action/replication_record"}},"created_at":"2026-05-18T00:07:45.742486+00:00","updated_at":"2026-05-18T00:07:45.742486+00:00"}