{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:FXDFC6ILI5BTOGLHOQ3BECWXHM","short_pith_number":"pith:FXDFC6IL","canonical_record":{"source":{"id":"1708.05509","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-18T04:57:28Z","cross_cats_sorted":[],"title_canon_sha256":"aec27bb407cc34d185e3aac4aa088095679e87596171c188c2b7e77900dbf292","abstract_canon_sha256":"422cb31062e54f5ddd0c128cc1dea60beae52cae84023f3b655998746aa0678a"},"schema_version":"1.0"},"canonical_sha256":"2dc651790b47433719677436120ad73b2618045d742fc93458ff4ea649bf1916","source":{"kind":"arxiv","id":"1708.05509","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.05509","created_at":"2026-05-18T00:37:49Z"},{"alias_kind":"arxiv_version","alias_value":"1708.05509v1","created_at":"2026-05-18T00:37:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.05509","created_at":"2026-05-18T00:37:49Z"},{"alias_kind":"pith_short_12","alias_value":"FXDFC6ILI5BT","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_16","alias_value":"FXDFC6ILI5BTOGLH","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_8","alias_value":"FXDFC6IL","created_at":"2026-05-18T12:31:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:FXDFC6ILI5BTOGLHOQ3BECWXHM","target":"record","payload":{"canonical_record":{"source":{"id":"1708.05509","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-18T04:57:28Z","cross_cats_sorted":[],"title_canon_sha256":"aec27bb407cc34d185e3aac4aa088095679e87596171c188c2b7e77900dbf292","abstract_canon_sha256":"422cb31062e54f5ddd0c128cc1dea60beae52cae84023f3b655998746aa0678a"},"schema_version":"1.0"},"canonical_sha256":"2dc651790b47433719677436120ad73b2618045d742fc93458ff4ea649bf1916","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:37:49.879049Z","signature_b64":"nxNKir7oDxk3InXIfju7JJFu+HaEp3HdP21t7X56IMXqTWCn5TJX4chtqsh5mOgTqJtxjhibi7QvDomkrP1QCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2dc651790b47433719677436120ad73b2618045d742fc93458ff4ea649bf1916","last_reissued_at":"2026-05-18T00:37:49.878445Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:37:49.878445Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1708.05509","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":"2026-05-18T00:37:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6yeAyY+IhPgNPluYoPw8ZZ85EGyhhViKeHVNO2d5cQTe/ZD+zAskQLqad5AZif/KKIlKHqh0Qg81svRhcSc2Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T11:22:31.594714Z"},"content_sha256":"798d35e7ee6242ea311d812cc6df5738d53b9a39e2e6eeea466a1c5316014a52","schema_version":"1.0","event_id":"sha256:798d35e7ee6242ea311d812cc6df5738d53b9a39e2e6eeea466a1c5316014a52"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:FXDFC6ILI5BTOGLHOQ3BECWXHM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Towards the Automatic Anime Characters Creation with Generative Adversarial Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Huachun Zhu, Jiakai Zhang, Minjun Li, Yanghua Jin, Yingtao Tian, Zhihao Fang","submitted_at":"2017-08-18T04:57:28Z","abstract_excerpt":"Automatic generation of facial images has been well studied after the Generative Adversarial Network (GAN) came out. There exists some attempts applying the GAN model to the problem of generating facial images of anime characters, but none of the existing work gives a promising result. In this work, we explore the training of GAN models specialized on an anime facial image dataset. We address the issue from both the data and the model aspect, by collecting a more clean, well-suited dataset and leverage proper, empirical application of DRAGAN. With quantitative analysis and case studies we demo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.05509","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":"2026-05-18T00:37:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kLAUCPBETarJYF8BmYK74l5JEIIcys0ZAlEzA4tbqCJ0oowALy5p0W9n72/dnIyXPVCD38SL6sZ0xIfd+fbGDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T11:22:31.595059Z"},"content_sha256":"ebe7498f99ad2c443ba6fbce77dd8c3bb843a3192d0232f331d247f9d6c3549a","schema_version":"1.0","event_id":"sha256:ebe7498f99ad2c443ba6fbce77dd8c3bb843a3192d0232f331d247f9d6c3549a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FXDFC6ILI5BTOGLHOQ3BECWXHM/bundle.json","state_url":"https://pith.science/pith/FXDFC6ILI5BTOGLHOQ3BECWXHM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FXDFC6ILI5BTOGLHOQ3BECWXHM/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":"2026-05-31T11:22:31Z","links":{"resolver":"https://pith.science/pith/FXDFC6ILI5BTOGLHOQ3BECWXHM","bundle":"https://pith.science/pith/FXDFC6ILI5BTOGLHOQ3BECWXHM/bundle.json","state":"https://pith.science/pith/FXDFC6ILI5BTOGLHOQ3BECWXHM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FXDFC6ILI5BTOGLHOQ3BECWXHM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:FXDFC6ILI5BTOGLHOQ3BECWXHM","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":"422cb31062e54f5ddd0c128cc1dea60beae52cae84023f3b655998746aa0678a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-18T04:57:28Z","title_canon_sha256":"aec27bb407cc34d185e3aac4aa088095679e87596171c188c2b7e77900dbf292"},"schema_version":"1.0","source":{"id":"1708.05509","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.05509","created_at":"2026-05-18T00:37:49Z"},{"alias_kind":"arxiv_version","alias_value":"1708.05509v1","created_at":"2026-05-18T00:37:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.05509","created_at":"2026-05-18T00:37:49Z"},{"alias_kind":"pith_short_12","alias_value":"FXDFC6ILI5BT","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_16","alias_value":"FXDFC6ILI5BTOGLH","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_8","alias_value":"FXDFC6IL","created_at":"2026-05-18T12:31:15Z"}],"graph_snapshots":[{"event_id":"sha256:ebe7498f99ad2c443ba6fbce77dd8c3bb843a3192d0232f331d247f9d6c3549a","target":"graph","created_at":"2026-05-18T00:37:49Z","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":"Automatic generation of facial images has been well studied after the Generative Adversarial Network (GAN) came out. There exists some attempts applying the GAN model to the problem of generating facial images of anime characters, but none of the existing work gives a promising result. In this work, we explore the training of GAN models specialized on an anime facial image dataset. We address the issue from both the data and the model aspect, by collecting a more clean, well-suited dataset and leverage proper, empirical application of DRAGAN. With quantitative analysis and case studies we demo","authors_text":"Huachun Zhu, Jiakai Zhang, Minjun Li, Yanghua Jin, Yingtao Tian, Zhihao Fang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-18T04:57:28Z","title":"Towards the Automatic Anime Characters Creation with Generative Adversarial Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.05509","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":"sha256:798d35e7ee6242ea311d812cc6df5738d53b9a39e2e6eeea466a1c5316014a52","target":"record","created_at":"2026-05-18T00:37:49Z","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":"422cb31062e54f5ddd0c128cc1dea60beae52cae84023f3b655998746aa0678a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-18T04:57:28Z","title_canon_sha256":"aec27bb407cc34d185e3aac4aa088095679e87596171c188c2b7e77900dbf292"},"schema_version":"1.0","source":{"id":"1708.05509","kind":"arxiv","version":1}},"canonical_sha256":"2dc651790b47433719677436120ad73b2618045d742fc93458ff4ea649bf1916","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2dc651790b47433719677436120ad73b2618045d742fc93458ff4ea649bf1916","first_computed_at":"2026-05-18T00:37:49.878445Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:37:49.878445Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"nxNKir7oDxk3InXIfju7JJFu+HaEp3HdP21t7X56IMXqTWCn5TJX4chtqsh5mOgTqJtxjhibi7QvDomkrP1QCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:37:49.879049Z","signed_message":"canonical_sha256_bytes"},"source_id":"1708.05509","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:798d35e7ee6242ea311d812cc6df5738d53b9a39e2e6eeea466a1c5316014a52","sha256:ebe7498f99ad2c443ba6fbce77dd8c3bb843a3192d0232f331d247f9d6c3549a"],"state_sha256":"dca635b0bc619024b4f88dc5b58bb4273962ff3b67d20ecf77e41f30930b1ed4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"X1/29zeYqyBTNlqpz7eN4P0ASNfmFJ/PEdDK5sR0rF3iVikn/x1FBtSs9vQEIxvAxpITWXsxi90+KI6r0Be1Bg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T11:22:31.597174Z","bundle_sha256":"ed99b1132fc880c9f22917e6e974efe2de437b8955108c3647da73550ce99a06"}}