{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:BEQMM4Y26SWQ7XCE3LJCFTO5ET","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"57158940f4109b998c513e9ac1ad4004c0e4c7212bc0dbca4e3b751f0a358a8b","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-11-30T23:31:28Z","title_canon_sha256":"3cf3ddcde8570362d94d271a675f840677a5ac898c426e789593fe65645f6ba1"},"schema_version":"1.0","source":{"id":"1712.00123","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.00123","created_at":"2026-05-18T00:29:07Z"},{"alias_kind":"arxiv_version","alias_value":"1712.00123v1","created_at":"2026-05-18T00:29:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.00123","created_at":"2026-05-18T00:29:07Z"},{"alias_kind":"pith_short_12","alias_value":"BEQMM4Y26SWQ","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_16","alias_value":"BEQMM4Y26SWQ7XCE","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_8","alias_value":"BEQMM4Y2","created_at":"2026-05-18T12:31:08Z"}],"graph_snapshots":[{"event_id":"sha256:f52482e4a11885304ece1646aedc4542f27b755d5b670b41b1bca16e5fb964cf","target":"graph","created_at":"2026-05-18T00:29:07Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"We propose a framework that learns a representation transferable across different domains and tasks in a label efficient manner. Our approach battles domain shift with a domain adversarial loss, and generalizes the embedding to novel task using a metric learning-based approach. Our model is simultaneously optimized on labeled source data and unlabeled or sparsely labeled data in the target domain. Our method shows compelling results on novel classes within a new domain even when only a few labeled examples per class are available, outperforming the prevalent fine-tuning approach. In addition, ","authors_text":"Judy Hoffman, Li Fei-Fei, Yuliang Zou, Zelun Luo","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-11-30T23:31:28Z","title":"Label Efficient Learning of Transferable Representations across Domains and Tasks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.00123","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:d2e9eaaba95dbfd8c6eb9999de4f66287c959a42900e830b6afea39a20dc4e60","target":"record","created_at":"2026-05-18T00:29:07Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"57158940f4109b998c513e9ac1ad4004c0e4c7212bc0dbca4e3b751f0a358a8b","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-11-30T23:31:28Z","title_canon_sha256":"3cf3ddcde8570362d94d271a675f840677a5ac898c426e789593fe65645f6ba1"},"schema_version":"1.0","source":{"id":"1712.00123","kind":"arxiv","version":1}},"canonical_sha256":"0920c6731af4ad0fdc44dad222cddd24f4366fd77ce4d3e52fb9cf76fc7a3f67","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0920c6731af4ad0fdc44dad222cddd24f4366fd77ce4d3e52fb9cf76fc7a3f67","first_computed_at":"2026-05-18T00:29:07.280142Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:29:07.280142Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vDwp4vOkI0aRgPIb4f7lGUG+0kBV5ZH/0nK1jKRvVWYn35A/TI+uj5NostSCGNAlTVZlEaDk/ja5sJ9VVRe3Cw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:29:07.281027Z","signed_message":"canonical_sha256_bytes"},"source_id":"1712.00123","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d2e9eaaba95dbfd8c6eb9999de4f66287c959a42900e830b6afea39a20dc4e60","sha256:f52482e4a11885304ece1646aedc4542f27b755d5b670b41b1bca16e5fb964cf"],"state_sha256":"331efacce11b720f279323b4aba2d620fa1bc88df9f8caed841b5cfda16be828"}