{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:OM365CORGFPXZMQL5XGFOCSPQP","short_pith_number":"pith:OM365COR","canonical_record":{"source":{"id":"1802.06454","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-18T22:16:19Z","cross_cats_sorted":[],"title_canon_sha256":"dbf9cc16f98ef9dab63e3cd1333b0d5407e91b847e900ba547308ed15070931a","abstract_canon_sha256":"3b9b25da68b318e6720ca49abbe8e476eb1f6e607646f50b201c30f22d823734"},"schema_version":"1.0"},"canonical_sha256":"7337ee89d1315f7cb20bedcc570a4f83d60f1fae7676637f28e2cc2144d16fa9","source":{"kind":"arxiv","id":"1802.06454","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.06454","created_at":"2026-05-18T00:23:03Z"},{"alias_kind":"arxiv_version","alias_value":"1802.06454v1","created_at":"2026-05-18T00:23:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.06454","created_at":"2026-05-18T00:23:03Z"},{"alias_kind":"pith_short_12","alias_value":"OM365CORGFPX","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"OM365CORGFPXZMQL","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"OM365COR","created_at":"2026-05-18T12:32:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:OM365CORGFPXZMQL5XGFOCSPQP","target":"record","payload":{"canonical_record":{"source":{"id":"1802.06454","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-18T22:16:19Z","cross_cats_sorted":[],"title_canon_sha256":"dbf9cc16f98ef9dab63e3cd1333b0d5407e91b847e900ba547308ed15070931a","abstract_canon_sha256":"3b9b25da68b318e6720ca49abbe8e476eb1f6e607646f50b201c30f22d823734"},"schema_version":"1.0"},"canonical_sha256":"7337ee89d1315f7cb20bedcc570a4f83d60f1fae7676637f28e2cc2144d16fa9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:23:03.250250Z","signature_b64":"8qE6GhnGOjWwMLo7WZJq+MjfDeXxcozs4bYs6F4EsXL6bTdc3usCwTNygBvrCdcTQa0vAXHb2Es4rh9fksPrAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7337ee89d1315f7cb20bedcc570a4f83d60f1fae7676637f28e2cc2144d16fa9","last_reissued_at":"2026-05-18T00:23:03.249571Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:23:03.249571Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1802.06454","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:23:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ys49mUtlkUf7G6VTe9QbZhZFKuf4UL3jcP9w08XUc2TwPpauAQXlMu8P+pn6LfPJizIL21QrD3FSHnTaPWdPCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-24T21:19:44.323069Z"},"content_sha256":"3221eeb91ac008e4bbb9a3ea334539819b7a20fc98180e7a1d301d0f4435d5c8","schema_version":"1.0","event_id":"sha256:3221eeb91ac008e4bbb9a3ea334539819b7a20fc98180e7a1d301d0f4435d5c8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:OM365CORGFPXZMQL5XGFOCSPQP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DA-GAN: Instance-level Image Translation by Deep Attention Generative Adversarial Networks (with Supplementary Materials)","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chang Wen Chen, Jianlong Fu, Shuang Ma, Tao Mei","submitted_at":"2018-02-18T22:16:19Z","abstract_excerpt":"Unsupervised image translation, which aims in translating two independent sets of images, is challenging in discovering the correct correspondences without paired data. Existing works build upon Generative Adversarial Network (GAN) such that the distribution of the translated images are indistinguishable from the distribution of the target set. However, such set-level constraints cannot learn the instance-level correspondences (e.g. aligned semantic parts in object configuration task). This limitation often results in false positives (e.g. geometric or semantic artifacts), and further leads to"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.06454","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:23:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"n76pT35betdubocVaBy1A5mvogGvy8IBu09ok+HjDQzdHQ2LQyMs495AB+r4nEuuyDAueRS5pX2Dsk146sGpAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-24T21:19:44.323744Z"},"content_sha256":"4efd8ba4fb788073bfe2cdcb7bb867dade7a4f24dec3c8876297dd557e97e0de","schema_version":"1.0","event_id":"sha256:4efd8ba4fb788073bfe2cdcb7bb867dade7a4f24dec3c8876297dd557e97e0de"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OM365CORGFPXZMQL5XGFOCSPQP/bundle.json","state_url":"https://pith.science/pith/OM365CORGFPXZMQL5XGFOCSPQP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OM365CORGFPXZMQL5XGFOCSPQP/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":"2026-05-24T21:19:44Z","links":{"resolver":"https://pith.science/pith/OM365CORGFPXZMQL5XGFOCSPQP","bundle":"https://pith.science/pith/OM365CORGFPXZMQL5XGFOCSPQP/bundle.json","state":"https://pith.science/pith/OM365CORGFPXZMQL5XGFOCSPQP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OM365CORGFPXZMQL5XGFOCSPQP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:OM365CORGFPXZMQL5XGFOCSPQP","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":"3b9b25da68b318e6720ca49abbe8e476eb1f6e607646f50b201c30f22d823734","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-18T22:16:19Z","title_canon_sha256":"dbf9cc16f98ef9dab63e3cd1333b0d5407e91b847e900ba547308ed15070931a"},"schema_version":"1.0","source":{"id":"1802.06454","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.06454","created_at":"2026-05-18T00:23:03Z"},{"alias_kind":"arxiv_version","alias_value":"1802.06454v1","created_at":"2026-05-18T00:23:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.06454","created_at":"2026-05-18T00:23:03Z"},{"alias_kind":"pith_short_12","alias_value":"OM365CORGFPX","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"OM365CORGFPXZMQL","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"OM365COR","created_at":"2026-05-18T12:32:43Z"}],"graph_snapshots":[{"event_id":"sha256:4efd8ba4fb788073bfe2cdcb7bb867dade7a4f24dec3c8876297dd557e97e0de","target":"graph","created_at":"2026-05-18T00:23:03Z","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":"Unsupervised image translation, which aims in translating two independent sets of images, is challenging in discovering the correct correspondences without paired data. Existing works build upon Generative Adversarial Network (GAN) such that the distribution of the translated images are indistinguishable from the distribution of the target set. However, such set-level constraints cannot learn the instance-level correspondences (e.g. aligned semantic parts in object configuration task). This limitation often results in false positives (e.g. geometric or semantic artifacts), and further leads to","authors_text":"Chang Wen Chen, Jianlong Fu, Shuang Ma, Tao Mei","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-18T22:16:19Z","title":"DA-GAN: Instance-level Image Translation by Deep Attention Generative Adversarial Networks (with Supplementary Materials)"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.06454","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":"sha256:3221eeb91ac008e4bbb9a3ea334539819b7a20fc98180e7a1d301d0f4435d5c8","target":"record","created_at":"2026-05-18T00:23:03Z","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":"3b9b25da68b318e6720ca49abbe8e476eb1f6e607646f50b201c30f22d823734","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-18T22:16:19Z","title_canon_sha256":"dbf9cc16f98ef9dab63e3cd1333b0d5407e91b847e900ba547308ed15070931a"},"schema_version":"1.0","source":{"id":"1802.06454","kind":"arxiv","version":1}},"canonical_sha256":"7337ee89d1315f7cb20bedcc570a4f83d60f1fae7676637f28e2cc2144d16fa9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7337ee89d1315f7cb20bedcc570a4f83d60f1fae7676637f28e2cc2144d16fa9","first_computed_at":"2026-05-18T00:23:03.249571Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:23:03.249571Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8qE6GhnGOjWwMLo7WZJq+MjfDeXxcozs4bYs6F4EsXL6bTdc3usCwTNygBvrCdcTQa0vAXHb2Es4rh9fksPrAQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:23:03.250250Z","signed_message":"canonical_sha256_bytes"},"source_id":"1802.06454","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3221eeb91ac008e4bbb9a3ea334539819b7a20fc98180e7a1d301d0f4435d5c8","sha256:4efd8ba4fb788073bfe2cdcb7bb867dade7a4f24dec3c8876297dd557e97e0de"],"state_sha256":"925019f63d3ffcb1703766ff2cbc8877a7d11d49f90c9cf83278649c4c54fd85"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7PvJrb+CGlGysI2jGIlVqxFJqyVle7NbY0QpJei20iWYH1SHCAnzqZPdb3A9aT7EC/ZIbMs6SkOTK5zbubhYBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-24T21:19:44.327386Z","bundle_sha256":"54fec0e2ff84d5e655d4d79c9eaf5c1a7b27391776a78d8f89af67584daf91fd"}}