{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:C6KZFCZMEUBGFBKBIKYZDIQMZJ","short_pith_number":"pith:C6KZFCZM","schema_version":"1.0","canonical_sha256":"1795928b2c250262854142b191a20cca6e0929f5d2f5cd4fb9c82fad60e3f304","source":{"kind":"arxiv","id":"1811.00445","version":2},"attestation_state":"computed","paper":{"title":"CariGAN: Caricature Generation through Weakly Paired Adversarial Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Haofu Liao, Jiebo Luo, Jing Huo, Wei Xiong, Wenbin Li, Yang Gao","submitted_at":"2018-11-01T15:40:23Z","abstract_excerpt":"Caricature generation is an interesting yet challenging task. The primary goal is to generate plausible caricatures with reasonable exaggerations given face images. Conventional caricature generation approaches mainly use low-level geometric transformations such as image warping to generate exaggerated images, which lack richness and diversity in terms of content and style. The recent progress in generative adversarial networks (GANs) makes it possible to learn an image-to-image transformation from data, so that richer contents and styles can be generated. However, directly applying the GAN-ba"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1811.00445","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-01T15:40:23Z","cross_cats_sorted":[],"title_canon_sha256":"29e79af3f87d96e33a4dfd9d57c2cf3437b5745a0bd79626285067a8edcb4f63","abstract_canon_sha256":"142b7c110dab27418eeab211d83755f3ba481bdc7f177dacea42eeab7b972e54"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:12.690171Z","signature_b64":"VsTxvrB5CJKFkC/Wb4AK590OX+vLu7bwpxwZfNHLDVq4aRHkqOxiFnruJWpllIK4+Cpf9ZJqVf5FkirhFW+EDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1795928b2c250262854142b191a20cca6e0929f5d2f5cd4fb9c82fad60e3f304","last_reissued_at":"2026-05-18T00:00:12.689610Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:12.689610Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CariGAN: Caricature Generation through Weakly Paired Adversarial Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Haofu Liao, Jiebo Luo, Jing Huo, Wei Xiong, Wenbin Li, Yang Gao","submitted_at":"2018-11-01T15:40:23Z","abstract_excerpt":"Caricature generation is an interesting yet challenging task. The primary goal is to generate plausible caricatures with reasonable exaggerations given face images. Conventional caricature generation approaches mainly use low-level geometric transformations such as image warping to generate exaggerated images, which lack richness and diversity in terms of content and style. The recent progress in generative adversarial networks (GANs) makes it possible to learn an image-to-image transformation from data, so that richer contents and styles can be generated. However, directly applying the GAN-ba"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.00445","kind":"arxiv","version":2},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1811.00445","created_at":"2026-05-18T00:00:12.689685+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.00445v2","created_at":"2026-05-18T00:00:12.689685+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.00445","created_at":"2026-05-18T00:00:12.689685+00:00"},{"alias_kind":"pith_short_12","alias_value":"C6KZFCZMEUBG","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_16","alias_value":"C6KZFCZMEUBGFBKB","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_8","alias_value":"C6KZFCZM","created_at":"2026-05-18T12:32:16.446611+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/C6KZFCZMEUBGFBKBIKYZDIQMZJ","json":"https://pith.science/pith/C6KZFCZMEUBGFBKBIKYZDIQMZJ.json","graph_json":"https://pith.science/api/pith-number/C6KZFCZMEUBGFBKBIKYZDIQMZJ/graph.json","events_json":"https://pith.science/api/pith-number/C6KZFCZMEUBGFBKBIKYZDIQMZJ/events.json","paper":"https://pith.science/paper/C6KZFCZM"},"agent_actions":{"view_html":"https://pith.science/pith/C6KZFCZMEUBGFBKBIKYZDIQMZJ","download_json":"https://pith.science/pith/C6KZFCZMEUBGFBKBIKYZDIQMZJ.json","view_paper":"https://pith.science/paper/C6KZFCZM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.00445&json=true","fetch_graph":"https://pith.science/api/pith-number/C6KZFCZMEUBGFBKBIKYZDIQMZJ/graph.json","fetch_events":"https://pith.science/api/pith-number/C6KZFCZMEUBGFBKBIKYZDIQMZJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/C6KZFCZMEUBGFBKBIKYZDIQMZJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/C6KZFCZMEUBGFBKBIKYZDIQMZJ/action/storage_attestation","attest_author":"https://pith.science/pith/C6KZFCZMEUBGFBKBIKYZDIQMZJ/action/author_attestation","sign_citation":"https://pith.science/pith/C6KZFCZMEUBGFBKBIKYZDIQMZJ/action/citation_signature","submit_replication":"https://pith.science/pith/C6KZFCZMEUBGFBKBIKYZDIQMZJ/action/replication_record"}},"created_at":"2026-05-18T00:00:12.689685+00:00","updated_at":"2026-05-18T00:00:12.689685+00:00"}