{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:YLBG6YBQXUEWQM3UGI7QQ5C6WS","short_pith_number":"pith:YLBG6YBQ","canonical_record":{"source":{"id":"1911.06460","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2019-11-15T03:09:38Z","cross_cats_sorted":[],"title_canon_sha256":"368e16166d510794bb6201d8c775f5f73113ef27a1b56da6ca46dae3e4bb39dd","abstract_canon_sha256":"6523479f85b7a54cdf8868e4f30b4d46ae7c07418346630eda2837020895cccb"},"schema_version":"1.0"},"canonical_sha256":"c2c26f6030bd09683374323f08745eb4ab0bc411634f6ff8ec5258d426bdbf08","source":{"kind":"arxiv","id":"1911.06460","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1911.06460","created_at":"2026-07-05T00:19:23Z"},{"alias_kind":"arxiv_version","alias_value":"1911.06460v1","created_at":"2026-07-05T00:19:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.06460","created_at":"2026-07-05T00:19:23Z"},{"alias_kind":"pith_short_12","alias_value":"YLBG6YBQXUEW","created_at":"2026-07-05T00:19:23Z"},{"alias_kind":"pith_short_16","alias_value":"YLBG6YBQXUEWQM3U","created_at":"2026-07-05T00:19:23Z"},{"alias_kind":"pith_short_8","alias_value":"YLBG6YBQ","created_at":"2026-07-05T00:19:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:YLBG6YBQXUEWQM3UGI7QQ5C6WS","target":"record","payload":{"canonical_record":{"source":{"id":"1911.06460","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2019-11-15T03:09:38Z","cross_cats_sorted":[],"title_canon_sha256":"368e16166d510794bb6201d8c775f5f73113ef27a1b56da6ca46dae3e4bb39dd","abstract_canon_sha256":"6523479f85b7a54cdf8868e4f30b4d46ae7c07418346630eda2837020895cccb"},"schema_version":"1.0"},"canonical_sha256":"c2c26f6030bd09683374323f08745eb4ab0bc411634f6ff8ec5258d426bdbf08","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:19:23.854104Z","signature_b64":"18KgFkgiBTiQaFovkqzSaBsHRVPlvVbAXj2UzXHkKZNhT0+x6PI6OcclkP5J09D1bT74mQ1fXu96H/iNP2u6DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c2c26f6030bd09683374323f08745eb4ab0bc411634f6ff8ec5258d426bdbf08","last_reissued_at":"2026-07-05T00:19:23.853751Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:19:23.853751Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1911.06460","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":"2026-07-05T00:19:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"04Jwc8x7DUbRcZiCmkrJs3U83GkF2WLrQh8CPgob9Jc3ybJT2nvgjjVSam/9zg8qQHJAsA8IQZHb4Mwoas3+Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T19:13:23.523991Z"},"content_sha256":"983086c49454ea662c47931cdd331a4e5d5bb69c540cba4f943254a4c697e309","schema_version":"1.0","event_id":"sha256:983086c49454ea662c47931cdd331a4e5d5bb69c540cba4f943254a4c697e309"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:YLBG6YBQXUEWQM3UGI7QQ5C6WS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Human Annotations Improve GAN Performances","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Juanyong Duan, Qi Zhao, Sim Heng Ong","submitted_at":"2019-11-15T03:09:38Z","abstract_excerpt":"Generative Adversarial Networks (GANs) have shown great success in many applications. In this work, we present a novel method that leverages human annotations to improve the quality of generated images. Unlike previous paradigms that directly ask annotators to distinguish between real and fake data in a straightforward way, we propose and annotate a set of carefully designed attributes that encode important image information at various levels, to understand the differences between fake and real images. Specifically, we have collected an annotated dataset that contains 600 fake images and 400 r"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.06460","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1911.06460/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T00:19:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0PQLVHQ72Ts3mNU4v+8Y3omnpkfIzecVI4684n4fDy6PVFQb+bClr0DYxVWr+oelFWhnzueIf7O4tCx9NNj/DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T19:13:23.524679Z"},"content_sha256":"10d45d4df4c40ece1eead07811d8586a2e3126add1aa132d51846b980b188e84","schema_version":"1.0","event_id":"sha256:10d45d4df4c40ece1eead07811d8586a2e3126add1aa132d51846b980b188e84"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YLBG6YBQXUEWQM3UGI7QQ5C6WS/bundle.json","state_url":"https://pith.science/pith/YLBG6YBQXUEWQM3UGI7QQ5C6WS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YLBG6YBQXUEWQM3UGI7QQ5C6WS/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":"2026-08-10T19:13:23Z","links":{"resolver":"https://pith.science/pith/YLBG6YBQXUEWQM3UGI7QQ5C6WS","bundle":"https://pith.science/pith/YLBG6YBQXUEWQM3UGI7QQ5C6WS/bundle.json","state":"https://pith.science/pith/YLBG6YBQXUEWQM3UGI7QQ5C6WS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YLBG6YBQXUEWQM3UGI7QQ5C6WS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:YLBG6YBQXUEWQM3UGI7QQ5C6WS","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":"6523479f85b7a54cdf8868e4f30b4d46ae7c07418346630eda2837020895cccb","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2019-11-15T03:09:38Z","title_canon_sha256":"368e16166d510794bb6201d8c775f5f73113ef27a1b56da6ca46dae3e4bb39dd"},"schema_version":"1.0","source":{"id":"1911.06460","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1911.06460","created_at":"2026-07-05T00:19:23Z"},{"alias_kind":"arxiv_version","alias_value":"1911.06460v1","created_at":"2026-07-05T00:19:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.06460","created_at":"2026-07-05T00:19:23Z"},{"alias_kind":"pith_short_12","alias_value":"YLBG6YBQXUEW","created_at":"2026-07-05T00:19:23Z"},{"alias_kind":"pith_short_16","alias_value":"YLBG6YBQXUEWQM3U","created_at":"2026-07-05T00:19:23Z"},{"alias_kind":"pith_short_8","alias_value":"YLBG6YBQ","created_at":"2026-07-05T00:19:23Z"}],"graph_snapshots":[{"event_id":"sha256:10d45d4df4c40ece1eead07811d8586a2e3126add1aa132d51846b980b188e84","target":"graph","created_at":"2026-07-05T00:19:23Z","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/1911.06460/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Generative Adversarial Networks (GANs) have shown great success in many applications. In this work, we present a novel method that leverages human annotations to improve the quality of generated images. Unlike previous paradigms that directly ask annotators to distinguish between real and fake data in a straightforward way, we propose and annotate a set of carefully designed attributes that encode important image information at various levels, to understand the differences between fake and real images. Specifically, we have collected an annotated dataset that contains 600 fake images and 400 r","authors_text":"Juanyong Duan, Qi Zhao, Sim Heng Ong","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2019-11-15T03:09:38Z","title":"Human Annotations Improve GAN Performances"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.06460","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":"sha256:983086c49454ea662c47931cdd331a4e5d5bb69c540cba4f943254a4c697e309","target":"record","created_at":"2026-07-05T00:19:23Z","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":"6523479f85b7a54cdf8868e4f30b4d46ae7c07418346630eda2837020895cccb","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2019-11-15T03:09:38Z","title_canon_sha256":"368e16166d510794bb6201d8c775f5f73113ef27a1b56da6ca46dae3e4bb39dd"},"schema_version":"1.0","source":{"id":"1911.06460","kind":"arxiv","version":1}},"canonical_sha256":"c2c26f6030bd09683374323f08745eb4ab0bc411634f6ff8ec5258d426bdbf08","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c2c26f6030bd09683374323f08745eb4ab0bc411634f6ff8ec5258d426bdbf08","first_computed_at":"2026-07-05T00:19:23.853751Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:19:23.853751Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"18KgFkgiBTiQaFovkqzSaBsHRVPlvVbAXj2UzXHkKZNhT0+x6PI6OcclkP5J09D1bT74mQ1fXu96H/iNP2u6DA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:19:23.854104Z","signed_message":"canonical_sha256_bytes"},"source_id":"1911.06460","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:983086c49454ea662c47931cdd331a4e5d5bb69c540cba4f943254a4c697e309","sha256:10d45d4df4c40ece1eead07811d8586a2e3126add1aa132d51846b980b188e84"],"state_sha256":"ec115a5a8de94501f84e925f817059d211b9a22b340c84e398677027db73466a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8QFQdamq9OIqUT/J8qchYJRQLBM4ZS2hftvGrDQmp4c4FbZF2XA4wxGgSYIaBGGHFW1PhxU89L+b0LjIj7/eDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T19:13:23.536397Z","bundle_sha256":"75b4fb5de6b9ec1e782e6a1f29a25539db5ef913efd186deabff3bbd37536b00"}}