{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:HNPG7NRLQN33UQVV4IZMAMKAXW","short_pith_number":"pith:HNPG7NRL","schema_version":"1.0","canonical_sha256":"3b5e6fb62b8377ba42b5e232c03140bd9ffd65ac086585d96d4d48c192973b3e","source":{"kind":"arxiv","id":"1711.10125","version":3},"attestation_state":"computed","paper":{"title":"Learning to cluster in order to transfer across domains and tasks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Yen-Chang Hsu, Zhaoyang Lv, Zsolt Kira","submitted_at":"2017-11-28T04:59:58Z","abstract_excerpt":"This paper introduces a novel method to perform transfer learning across domains and tasks, formulating it as a problem of learning to cluster. The key insight is that, in addition to features, we can transfer similarity information and this is sufficient to learn a similarity function and clustering network to perform both domain adaptation and cross-task transfer learning. We begin by reducing categorical information to pairwise constraints, which only considers whether two instances belong to the same class or not. This similarity is category-agnostic and can be learned from data in the sou"},"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":"1711.10125","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-28T04:59:58Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"47476680ff7762df397669a2e04ee62381ed3a3596a6eb1b0f3f0a127b8fc2f4","abstract_canon_sha256":"eaf851b6ab101ecb32d1e1ab5368007ce1932ee996f592b44f0a5862f70d57cf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:20:46.443016Z","signature_b64":"iMlsHlfu8lIc58u43OmPOd7s7xyKm5kR/oOGQeBmunSznP4V5RnBnRvlhvSwsYGBARPu/EUJ3IQ8e6cuqKdBDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3b5e6fb62b8377ba42b5e232c03140bd9ffd65ac086585d96d4d48c192973b3e","last_reissued_at":"2026-05-18T00:20:46.442037Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:20:46.442037Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning to cluster in order to transfer across domains and tasks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Yen-Chang Hsu, Zhaoyang Lv, Zsolt Kira","submitted_at":"2017-11-28T04:59:58Z","abstract_excerpt":"This paper introduces a novel method to perform transfer learning across domains and tasks, formulating it as a problem of learning to cluster. The key insight is that, in addition to features, we can transfer similarity information and this is sufficient to learn a similarity function and clustering network to perform both domain adaptation and cross-task transfer learning. We begin by reducing categorical information to pairwise constraints, which only considers whether two instances belong to the same class or not. This similarity is category-agnostic and can be learned from data in the sou"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.10125","kind":"arxiv","version":3},"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":"1711.10125","created_at":"2026-05-18T00:20:46.442583+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.10125v3","created_at":"2026-05-18T00:20:46.442583+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.10125","created_at":"2026-05-18T00:20:46.442583+00:00"},{"alias_kind":"pith_short_12","alias_value":"HNPG7NRLQN33","created_at":"2026-05-18T12:31:18.294218+00:00"},{"alias_kind":"pith_short_16","alias_value":"HNPG7NRLQN33UQVV","created_at":"2026-05-18T12:31:18.294218+00:00"},{"alias_kind":"pith_short_8","alias_value":"HNPG7NRL","created_at":"2026-05-18T12:31:18.294218+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.10670","citing_title":"IntentGPT: Few-shot Intent Discovery with Large Language Models","ref_index":22,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HNPG7NRLQN33UQVV4IZMAMKAXW","json":"https://pith.science/pith/HNPG7NRLQN33UQVV4IZMAMKAXW.json","graph_json":"https://pith.science/api/pith-number/HNPG7NRLQN33UQVV4IZMAMKAXW/graph.json","events_json":"https://pith.science/api/pith-number/HNPG7NRLQN33UQVV4IZMAMKAXW/events.json","paper":"https://pith.science/paper/HNPG7NRL"},"agent_actions":{"view_html":"https://pith.science/pith/HNPG7NRLQN33UQVV4IZMAMKAXW","download_json":"https://pith.science/pith/HNPG7NRLQN33UQVV4IZMAMKAXW.json","view_paper":"https://pith.science/paper/HNPG7NRL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.10125&json=true","fetch_graph":"https://pith.science/api/pith-number/HNPG7NRLQN33UQVV4IZMAMKAXW/graph.json","fetch_events":"https://pith.science/api/pith-number/HNPG7NRLQN33UQVV4IZMAMKAXW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HNPG7NRLQN33UQVV4IZMAMKAXW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HNPG7NRLQN33UQVV4IZMAMKAXW/action/storage_attestation","attest_author":"https://pith.science/pith/HNPG7NRLQN33UQVV4IZMAMKAXW/action/author_attestation","sign_citation":"https://pith.science/pith/HNPG7NRLQN33UQVV4IZMAMKAXW/action/citation_signature","submit_replication":"https://pith.science/pith/HNPG7NRLQN33UQVV4IZMAMKAXW/action/replication_record"}},"created_at":"2026-05-18T00:20:46.442583+00:00","updated_at":"2026-05-18T00:20:46.442583+00:00"}