{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:Y66TO6SZRTG3EBOI5QDWADRPIA","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":"20131c0fce23074445f21d1dd71c80eae9af63e46f3c3bc15cbf52873cd95773","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-24T23:34:06Z","title_canon_sha256":"c54dcc71c4449ed38b18271e1da9e11db39180beace275d0f801fc8822413db7"},"schema_version":"1.0","source":{"id":"1803.09180","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.09180","created_at":"2026-05-18T00:20:12Z"},{"alias_kind":"arxiv_version","alias_value":"1803.09180v1","created_at":"2026-05-18T00:20:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.09180","created_at":"2026-05-18T00:20:12Z"},{"alias_kind":"pith_short_12","alias_value":"Y66TO6SZRTG3","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_16","alias_value":"Y66TO6SZRTG3EBOI","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_8","alias_value":"Y66TO6SZ","created_at":"2026-05-18T12:33:04Z"}],"graph_snapshots":[{"event_id":"sha256:55782b4a0d7478005102b93c80dce2fa1927d2d268fac39695f0e457901ad9fa","target":"graph","created_at":"2026-05-18T00:20:12Z","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":"Large-scale labeled training datasets have enabled deep neural networks to excel on a wide range of benchmark vision tasks. However, in many applications it is prohibitively expensive or time-consuming to obtain large quantities of labeled data. To cope with limited labeled training data, many have attempted to directly apply models trained on a large-scale labeled source domain to another sparsely labeled target domain. Unfortunately, direct transfer across domains often performs poorly due to domain shift and dataset bias. Domain adaptation is the machine learning paradigm that aims to learn","authors_text":"Bichen Wu, Joseph Gonzalez, Kurt Keutzer, Sanjit A. Seshia, Sicheng Zhao","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-24T23:34:06Z","title":"Unsupervised Domain Adaptation: from Simulation Engine to the RealWorld"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.09180","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:e4e0b765fd945921eae97151e08f116969c7ea21e7a7348dea757cc5eb7e2aa2","target":"record","created_at":"2026-05-18T00:20:12Z","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":"20131c0fce23074445f21d1dd71c80eae9af63e46f3c3bc15cbf52873cd95773","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-24T23:34:06Z","title_canon_sha256":"c54dcc71c4449ed38b18271e1da9e11db39180beace275d0f801fc8822413db7"},"schema_version":"1.0","source":{"id":"1803.09180","kind":"arxiv","version":1}},"canonical_sha256":"c7bd377a598ccdb205c8ec07600e2f40051fb69404185d559f3a09a45261e0ba","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c7bd377a598ccdb205c8ec07600e2f40051fb69404185d559f3a09a45261e0ba","first_computed_at":"2026-05-18T00:20:12.379684Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:20:12.379684Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"IWKxlQOQziTKtMFXNSEoBlQVtMfr5PEhRnc8GdzMYBOS0XQ4EqQeZbT3t2QXmYFCSmI9ZZig2liPYmPdrlAcBw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:20:12.380313Z","signed_message":"canonical_sha256_bytes"},"source_id":"1803.09180","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e4e0b765fd945921eae97151e08f116969c7ea21e7a7348dea757cc5eb7e2aa2","sha256:55782b4a0d7478005102b93c80dce2fa1927d2d268fac39695f0e457901ad9fa"],"state_sha256":"2e122154004586b900e26b24b2746544929edd9be24861641e8d3424bf95045a"}