{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:EWFZLDLPI2GTKCDQAPFWMPCXTF","short_pith_number":"pith:EWFZLDLP","canonical_record":{"source":{"id":"2003.12137","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CV","submitted_at":"2020-03-26T20:17:55Z","cross_cats_sorted":[],"title_canon_sha256":"94cfe20e36c9fed456181aede4d610cbeaa92e49d78c4038f28d80fd0b7e9cf4","abstract_canon_sha256":"813ddaee5ffcb264d2b3f0df954474b8f7ca5ac229aa3f0edbfe8ef6ab94d98e"},"schema_version":"1.0"},"canonical_sha256":"258b958d6f468d35087003cb663c57995fe76d269b68b66251254840f9cf221c","source":{"kind":"arxiv","id":"2003.12137","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2003.12137","created_at":"2026-07-05T00:50:55Z"},{"alias_kind":"arxiv_version","alias_value":"2003.12137v1","created_at":"2026-07-05T00:50:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.12137","created_at":"2026-07-05T00:50:55Z"},{"alias_kind":"pith_short_12","alias_value":"EWFZLDLPI2GT","created_at":"2026-07-05T00:50:55Z"},{"alias_kind":"pith_short_16","alias_value":"EWFZLDLPI2GTKCDQ","created_at":"2026-07-05T00:50:55Z"},{"alias_kind":"pith_short_8","alias_value":"EWFZLDLP","created_at":"2026-07-05T00:50:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:EWFZLDLPI2GTKCDQAPFWMPCXTF","target":"record","payload":{"canonical_record":{"source":{"id":"2003.12137","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CV","submitted_at":"2020-03-26T20:17:55Z","cross_cats_sorted":[],"title_canon_sha256":"94cfe20e36c9fed456181aede4d610cbeaa92e49d78c4038f28d80fd0b7e9cf4","abstract_canon_sha256":"813ddaee5ffcb264d2b3f0df954474b8f7ca5ac229aa3f0edbfe8ef6ab94d98e"},"schema_version":"1.0"},"canonical_sha256":"258b958d6f468d35087003cb663c57995fe76d269b68b66251254840f9cf221c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:50:55.821471Z","signature_b64":"VNO4wAj/60864GpWVm8NrhWegPY7PXnTn4p0ZvWStDIL5wSx7TPhooGUZsYPae1CfY0wX+vy2DQ+NYHMRdH+BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"258b958d6f468d35087003cb663c57995fe76d269b68b66251254840f9cf221c","last_reissued_at":"2026-07-05T00:50:55.821091Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:50:55.821091Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2003.12137","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":"2026-07-05T00:50:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"v8uu9lyet7chlOhAncwlhWJnr1e7XcgOgBi/ACva1DmprEBXYHMI40l3f4nfxSOzxOXrxIly3LWuVzLsomNsCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T11:14:30.083106Z"},"content_sha256":"dfab6bd845b6de620cf7a8f90243b4b3f3d6852697a2b63a307677a2a92076ae","schema_version":"1.0","event_id":"sha256:dfab6bd845b6de620cf7a8f90243b4b3f3d6852697a2b63a307677a2a92076ae"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:EWFZLDLPI2GTKCDQAPFWMPCXTF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Cycle Text-To-Image GAN with BERT","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jason Li, Samir Sen, Trevor Tsue","submitted_at":"2020-03-26T20:17:55Z","abstract_excerpt":"We explore novel approaches to the task of image generation from their respective captions, building on state-of-the-art GAN architectures. Particularly, we baseline our models with the Attention-based GANs that learn attention mappings from words to image features. To better capture the features of the descriptions, we then built a novel cyclic design that learns an inverse function to maps the image back to original caption. Additionally, we incorporated recently developed BERT pretrained word embeddings as our initial text featurizer and observe a noticeable improvement in qualitative and q"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.12137","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/2003.12137/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":"2026-07-05T00:50:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JHMkphnH4ZTv67CqviFGAYIZ+GYfGsStV4o8wr3hyOByKd7lNBztmzZqzMpAXEI7kQ5MnppmYdn6Cs84+/FLDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T11:14:30.083495Z"},"content_sha256":"1897a4572265604073e6e1621086809b46e8f45e9db285b092bb9a5bfb422f9d","schema_version":"1.0","event_id":"sha256:1897a4572265604073e6e1621086809b46e8f45e9db285b092bb9a5bfb422f9d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EWFZLDLPI2GTKCDQAPFWMPCXTF/bundle.json","state_url":"https://pith.science/pith/EWFZLDLPI2GTKCDQAPFWMPCXTF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EWFZLDLPI2GTKCDQAPFWMPCXTF/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":"2026-07-09T11:14:30Z","links":{"resolver":"https://pith.science/pith/EWFZLDLPI2GTKCDQAPFWMPCXTF","bundle":"https://pith.science/pith/EWFZLDLPI2GTKCDQAPFWMPCXTF/bundle.json","state":"https://pith.science/pith/EWFZLDLPI2GTKCDQAPFWMPCXTF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EWFZLDLPI2GTKCDQAPFWMPCXTF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:EWFZLDLPI2GTKCDQAPFWMPCXTF","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":"813ddaee5ffcb264d2b3f0df954474b8f7ca5ac229aa3f0edbfe8ef6ab94d98e","cross_cats_sorted":[],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CV","submitted_at":"2020-03-26T20:17:55Z","title_canon_sha256":"94cfe20e36c9fed456181aede4d610cbeaa92e49d78c4038f28d80fd0b7e9cf4"},"schema_version":"1.0","source":{"id":"2003.12137","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2003.12137","created_at":"2026-07-05T00:50:55Z"},{"alias_kind":"arxiv_version","alias_value":"2003.12137v1","created_at":"2026-07-05T00:50:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.12137","created_at":"2026-07-05T00:50:55Z"},{"alias_kind":"pith_short_12","alias_value":"EWFZLDLPI2GT","created_at":"2026-07-05T00:50:55Z"},{"alias_kind":"pith_short_16","alias_value":"EWFZLDLPI2GTKCDQ","created_at":"2026-07-05T00:50:55Z"},{"alias_kind":"pith_short_8","alias_value":"EWFZLDLP","created_at":"2026-07-05T00:50:55Z"}],"graph_snapshots":[{"event_id":"sha256:1897a4572265604073e6e1621086809b46e8f45e9db285b092bb9a5bfb422f9d","target":"graph","created_at":"2026-07-05T00:50:55Z","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/2003.12137/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We explore novel approaches to the task of image generation from their respective captions, building on state-of-the-art GAN architectures. Particularly, we baseline our models with the Attention-based GANs that learn attention mappings from words to image features. To better capture the features of the descriptions, we then built a novel cyclic design that learns an inverse function to maps the image back to original caption. Additionally, we incorporated recently developed BERT pretrained word embeddings as our initial text featurizer and observe a noticeable improvement in qualitative and q","authors_text":"Jason Li, Samir Sen, Trevor Tsue","cross_cats":[],"headline":"","license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CV","submitted_at":"2020-03-26T20:17:55Z","title":"Cycle Text-To-Image GAN with BERT"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.12137","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:dfab6bd845b6de620cf7a8f90243b4b3f3d6852697a2b63a307677a2a92076ae","target":"record","created_at":"2026-07-05T00:50:55Z","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":"813ddaee5ffcb264d2b3f0df954474b8f7ca5ac229aa3f0edbfe8ef6ab94d98e","cross_cats_sorted":[],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CV","submitted_at":"2020-03-26T20:17:55Z","title_canon_sha256":"94cfe20e36c9fed456181aede4d610cbeaa92e49d78c4038f28d80fd0b7e9cf4"},"schema_version":"1.0","source":{"id":"2003.12137","kind":"arxiv","version":1}},"canonical_sha256":"258b958d6f468d35087003cb663c57995fe76d269b68b66251254840f9cf221c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"258b958d6f468d35087003cb663c57995fe76d269b68b66251254840f9cf221c","first_computed_at":"2026-07-05T00:50:55.821091Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:50:55.821091Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VNO4wAj/60864GpWVm8NrhWegPY7PXnTn4p0ZvWStDIL5wSx7TPhooGUZsYPae1CfY0wX+vy2DQ+NYHMRdH+BQ==","signature_status":"signed_v1","signed_at":"2026-07-05T00:50:55.821471Z","signed_message":"canonical_sha256_bytes"},"source_id":"2003.12137","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dfab6bd845b6de620cf7a8f90243b4b3f3d6852697a2b63a307677a2a92076ae","sha256:1897a4572265604073e6e1621086809b46e8f45e9db285b092bb9a5bfb422f9d"],"state_sha256":"430e3224389b299ab17eb2942510ca01f4c5e283bccb5b6d5cd391e7b8b1c354"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"c9qFuyAFSsVLWpCLA3fQ0TX43RMkEjE/JS9bTbljp3NewAHYYYWjL8lKNcTj5Bn6Vc4j/daWOqdudfvoUYNjAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-09T11:14:30.086517Z","bundle_sha256":"68213cf06994d6142a171211b2678d0997e4457d8cf526ad85d6a5a5bf063f38"}}