{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:LBMYOJHHHDSO3AO3SA5Y7HCWGL","short_pith_number":"pith:LBMYOJHH","canonical_record":{"source":{"id":"1612.05424","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-16T10:50:36Z","cross_cats_sorted":[],"title_canon_sha256":"282ab1df2def445c03d8a1797b75f525e56a1f46cb02e045e98b8b870d8b9d94","abstract_canon_sha256":"6247874c3dc3b0dd7934e2f4297834863a1acbf1d1efe666adb31871c6f52f02"},"schema_version":"1.0"},"canonical_sha256":"58598724e738e4ed81db903b8f9c5632e5c6bb27c6a0af841befbf3358fa7c08","source":{"kind":"arxiv","id":"1612.05424","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1612.05424","created_at":"2026-05-18T00:36:51Z"},{"alias_kind":"arxiv_version","alias_value":"1612.05424v2","created_at":"2026-05-18T00:36:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.05424","created_at":"2026-05-18T00:36:51Z"},{"alias_kind":"pith_short_12","alias_value":"LBMYOJHHHDSO","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_16","alias_value":"LBMYOJHHHDSO3AO3","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_8","alias_value":"LBMYOJHH","created_at":"2026-05-18T12:30:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:LBMYOJHHHDSO3AO3SA5Y7HCWGL","target":"record","payload":{"canonical_record":{"source":{"id":"1612.05424","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-16T10:50:36Z","cross_cats_sorted":[],"title_canon_sha256":"282ab1df2def445c03d8a1797b75f525e56a1f46cb02e045e98b8b870d8b9d94","abstract_canon_sha256":"6247874c3dc3b0dd7934e2f4297834863a1acbf1d1efe666adb31871c6f52f02"},"schema_version":"1.0"},"canonical_sha256":"58598724e738e4ed81db903b8f9c5632e5c6bb27c6a0af841befbf3358fa7c08","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:36:51.392828Z","signature_b64":"fve6qXIT97uoA4MRoxeGazBT/AfHy2poJDkzC+UvJV7rG0xg5uD5vbtvQx8VLfo1SRofIX9tki5XD/D7k523Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"58598724e738e4ed81db903b8f9c5632e5c6bb27c6a0af841befbf3358fa7c08","last_reissued_at":"2026-05-18T00:36:51.392210Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:36:51.392210Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1612.05424","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:36:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"II/Xv3yW4xTOBU2Kh2mnT21gZMJGVBblOHJ1E5nEB89KlidVjzvGm3KQDfQTlIdL4owmNS7X0Qkl8+KafUnrDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T02:53:49.366837Z"},"content_sha256":"70ae1551102a38bc729b8b8ab667585f37ac29cfb8e00ec81ede646921c1b586","schema_version":"1.0","event_id":"sha256:70ae1551102a38bc729b8b8ab667585f37ac29cfb8e00ec81ede646921c1b586"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:LBMYOJHHHDSO3AO3SA5Y7HCWGL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"David Dohan, Dilip Krishnan, Dumitru Erhan, Konstantinos Bousmalis, Nathan Silberman","submitted_at":"2016-12-16T10:50:36Z","abstract_excerpt":"Collecting well-annotated image datasets to train modern machine learning algorithms is prohibitively expensive for many tasks. One appealing alternative is rendering synthetic data where ground-truth annotations are generated automatically. Unfortunately, models trained purely on rendered images often fail to generalize to real images. To address this shortcoming, prior work introduced unsupervised domain adaptation algorithms that attempt to map representations between the two domains or learn to extract features that are domain-invariant. In this work, we present a new approach that learns,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.05424","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:36:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JYMXEdYwu5K9PTO/WRVTrxLOtRkcPdIraad6y2gElkLPWquS/spS47peg8NmsTV7MVB+dQP09SugS8HpuVAEDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T02:53:49.367587Z"},"content_sha256":"ce764a9ab29ef710b82f72485684a1709aa4118ed55d010dfb6cc0444ceb8dbf","schema_version":"1.0","event_id":"sha256:ce764a9ab29ef710b82f72485684a1709aa4118ed55d010dfb6cc0444ceb8dbf"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LBMYOJHHHDSO3AO3SA5Y7HCWGL/bundle.json","state_url":"https://pith.science/pith/LBMYOJHHHDSO3AO3SA5Y7HCWGL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LBMYOJHHHDSO3AO3SA5Y7HCWGL/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-26T02:53:49Z","links":{"resolver":"https://pith.science/pith/LBMYOJHHHDSO3AO3SA5Y7HCWGL","bundle":"https://pith.science/pith/LBMYOJHHHDSO3AO3SA5Y7HCWGL/bundle.json","state":"https://pith.science/pith/LBMYOJHHHDSO3AO3SA5Y7HCWGL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LBMYOJHHHDSO3AO3SA5Y7HCWGL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:LBMYOJHHHDSO3AO3SA5Y7HCWGL","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":"6247874c3dc3b0dd7934e2f4297834863a1acbf1d1efe666adb31871c6f52f02","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-16T10:50:36Z","title_canon_sha256":"282ab1df2def445c03d8a1797b75f525e56a1f46cb02e045e98b8b870d8b9d94"},"schema_version":"1.0","source":{"id":"1612.05424","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1612.05424","created_at":"2026-05-18T00:36:51Z"},{"alias_kind":"arxiv_version","alias_value":"1612.05424v2","created_at":"2026-05-18T00:36:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.05424","created_at":"2026-05-18T00:36:51Z"},{"alias_kind":"pith_short_12","alias_value":"LBMYOJHHHDSO","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_16","alias_value":"LBMYOJHHHDSO3AO3","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_8","alias_value":"LBMYOJHH","created_at":"2026-05-18T12:30:29Z"}],"graph_snapshots":[{"event_id":"sha256:ce764a9ab29ef710b82f72485684a1709aa4118ed55d010dfb6cc0444ceb8dbf","target":"graph","created_at":"2026-05-18T00:36:51Z","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":"Collecting well-annotated image datasets to train modern machine learning algorithms is prohibitively expensive for many tasks. One appealing alternative is rendering synthetic data where ground-truth annotations are generated automatically. Unfortunately, models trained purely on rendered images often fail to generalize to real images. To address this shortcoming, prior work introduced unsupervised domain adaptation algorithms that attempt to map representations between the two domains or learn to extract features that are domain-invariant. In this work, we present a new approach that learns,","authors_text":"David Dohan, Dilip Krishnan, Dumitru Erhan, Konstantinos Bousmalis, Nathan Silberman","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-16T10:50:36Z","title":"Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.05424","kind":"arxiv","version":2},"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":"sha256:70ae1551102a38bc729b8b8ab667585f37ac29cfb8e00ec81ede646921c1b586","target":"record","created_at":"2026-05-18T00:36:51Z","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":"6247874c3dc3b0dd7934e2f4297834863a1acbf1d1efe666adb31871c6f52f02","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-16T10:50:36Z","title_canon_sha256":"282ab1df2def445c03d8a1797b75f525e56a1f46cb02e045e98b8b870d8b9d94"},"schema_version":"1.0","source":{"id":"1612.05424","kind":"arxiv","version":2}},"canonical_sha256":"58598724e738e4ed81db903b8f9c5632e5c6bb27c6a0af841befbf3358fa7c08","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"58598724e738e4ed81db903b8f9c5632e5c6bb27c6a0af841befbf3358fa7c08","first_computed_at":"2026-05-18T00:36:51.392210Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:36:51.392210Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fve6qXIT97uoA4MRoxeGazBT/AfHy2poJDkzC+UvJV7rG0xg5uD5vbtvQx8VLfo1SRofIX9tki5XD/D7k523Cg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:36:51.392828Z","signed_message":"canonical_sha256_bytes"},"source_id":"1612.05424","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:70ae1551102a38bc729b8b8ab667585f37ac29cfb8e00ec81ede646921c1b586","sha256:ce764a9ab29ef710b82f72485684a1709aa4118ed55d010dfb6cc0444ceb8dbf"],"state_sha256":"b839920e58a384927f411228bd0362c298284977e29016d3d831742ce64a91f9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lgE/8Nq4KAUas3uWokDiuo2urfsYDNaH383fwl+B1zc6D4RkiW8/0WoDN2ezWHfRE2CsC3zGKPntvHKXMVjqBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T02:53:49.370696Z","bundle_sha256":"027316bbbeb74f878942a0b66daff448cc4398b8588791da02e9912c6b8b9428"}}