{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:RXXZKHUQWERW3RPIQZYKPS4EPC","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":"fe0394961fbb2bbcf2fe8c12b508c2c4ef98c131e360001c3840e600553c6d4e","cross_cats_sorted":["eess.IV"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2020-12-26T13:30:42Z","title_canon_sha256":"f2f17551829fe2d3c844b75fdfddfe4ba7e63cd025ad2f0436362db4639ce9ce"},"schema_version":"1.0","source":{"id":"2012.13736","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2012.13736","created_at":"2026-07-05T02:02:12Z"},{"alias_kind":"arxiv_version","alias_value":"2012.13736v1","created_at":"2026-07-05T02:02:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.13736","created_at":"2026-07-05T02:02:12Z"},{"alias_kind":"pith_short_12","alias_value":"RXXZKHUQWERW","created_at":"2026-07-05T02:02:12Z"},{"alias_kind":"pith_short_16","alias_value":"RXXZKHUQWERW3RPI","created_at":"2026-07-05T02:02:12Z"},{"alias_kind":"pith_short_8","alias_value":"RXXZKHUQ","created_at":"2026-07-05T02:02:12Z"}],"graph_snapshots":[{"event_id":"sha256:39dc62e4021c3c422b2db026a8925b2e59f75f61c693917de6db5d363731d273","target":"graph","created_at":"2026-07-05T02:02:12Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2012.13736/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Generative Adversarial Networks (GANs) have been extremely successful in various application domains such as computer vision, medicine, and natural language processing. Moreover, transforming an object or person to a desired shape become a well-studied research in the GANs. GANs are powerful models for learning complex distributions to synthesize semantically meaningful samples. However, there is a lack of comprehensive review in this field, especially lack of a collection of GANs loss-variant, evaluation metrics, remedies for diverse image generation, and stable training. Given the current fa","authors_text":"Eric Granger, Huiyu Zhou, Jie Yang, Masoumeh Zareapoor, M. Emre Celebi, Pourya Shamsolmoali, Ruili Wang","cross_cats":["eess.IV"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2020-12-26T13:30:42Z","title":"Image Synthesis with Adversarial Networks: a Comprehensive Survey and Case Studies"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.13736","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":"sha256:2aa9705a22eb644224df1d4cd946a093f72bcc5aafa67f6c89c39d5d9c8c56ea","target":"record","created_at":"2026-07-05T02:02:12Z","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":"fe0394961fbb2bbcf2fe8c12b508c2c4ef98c131e360001c3840e600553c6d4e","cross_cats_sorted":["eess.IV"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2020-12-26T13:30:42Z","title_canon_sha256":"f2f17551829fe2d3c844b75fdfddfe4ba7e63cd025ad2f0436362db4639ce9ce"},"schema_version":"1.0","source":{"id":"2012.13736","kind":"arxiv","version":1}},"canonical_sha256":"8def951e90b1236dc5e88670a7cb84789de63b5db512bc755b34bebd486c529f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8def951e90b1236dc5e88670a7cb84789de63b5db512bc755b34bebd486c529f","first_computed_at":"2026-07-05T02:02:12.500084Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:02:12.500084Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3AdL+Ajsj3EI7DKZlxFKHzFgk4fLiigeo77DD1Ckk0Ip0AszcsAsDXSqooaC9X3lsuhFdwTrpu3bQCtbEO/lDA==","signature_status":"signed_v1","signed_at":"2026-07-05T02:02:12.500471Z","signed_message":"canonical_sha256_bytes"},"source_id":"2012.13736","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2aa9705a22eb644224df1d4cd946a093f72bcc5aafa67f6c89c39d5d9c8c56ea","sha256:39dc62e4021c3c422b2db026a8925b2e59f75f61c693917de6db5d363731d273"],"state_sha256":"7b76758d965d0c94d0878367a7094ecbec2be2504bf69f06844b276a042f7124"}