{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:ZMMDCDYE27NJGVWOATKM5QCJZL","short_pith_number":"pith:ZMMDCDYE","schema_version":"1.0","canonical_sha256":"cb18310f04d7da9356ce04d4cec049caf3250ecc993733004228f8068eeab5d1","source":{"kind":"arxiv","id":"1705.09966","version":2},"attestation_state":"computed","paper":{"title":"Attribute-Guided Face Generation Using Conditional CycleGAN","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Chi-Keung Tang, Yongyi Lu, Yu-Wing Tai","submitted_at":"2017-05-28T17:37:23Z","abstract_excerpt":"We are interested in attribute-guided face generation: given a low-res face input image, an attribute vector that can be extracted from a high-res image (attribute image), our new method generates a high-res face image for the low-res input that satisfies the given attributes. To address this problem, we condition the CycleGAN and propose conditional CycleGAN, which is designed to 1) handle unpaired training data because the training low/high-res and high-res attribute images may not necessarily align with each other, and to 2) allow easy control of the appearance of the generated face via the"},"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":"1705.09966","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-28T17:37:23Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"b4d47047bbb4d5200259a6788f2b20115a87349dbf832c8b43192176097e8eba","abstract_canon_sha256":"c23718bde287e1c3868cf9240ef5de4ab73dde9423304b07a15d2e98f549b1a2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:45.326677Z","signature_b64":"LTKzHzg432i0YfZ+9DlYqp8D0/J596+0YlDiekYRgkJ6ikr4iOwV0LG4xSX89C5u/HFshqrzuHDcA8HR8ThxCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cb18310f04d7da9356ce04d4cec049caf3250ecc993733004228f8068eeab5d1","last_reissued_at":"2026-05-18T00:00:45.326230Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:45.326230Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Attribute-Guided Face Generation Using Conditional CycleGAN","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Chi-Keung Tang, Yongyi Lu, Yu-Wing Tai","submitted_at":"2017-05-28T17:37:23Z","abstract_excerpt":"We are interested in attribute-guided face generation: given a low-res face input image, an attribute vector that can be extracted from a high-res image (attribute image), our new method generates a high-res face image for the low-res input that satisfies the given attributes. To address this problem, we condition the CycleGAN and propose conditional CycleGAN, which is designed to 1) handle unpaired training data because the training low/high-res and high-res attribute images may not necessarily align with each other, and to 2) allow easy control of the appearance of the generated face via the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.09966","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":"1705.09966","created_at":"2026-05-18T00:00:45.326293+00:00"},{"alias_kind":"arxiv_version","alias_value":"1705.09966v2","created_at":"2026-05-18T00:00:45.326293+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.09966","created_at":"2026-05-18T00:00:45.326293+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZMMDCDYE27NJ","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZMMDCDYE27NJGVWO","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZMMDCDYE","created_at":"2026-05-18T12:31:59.375834+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1906.11979","citing_title":"A Utility-Preserving GAN for Face Obscuration","ref_index":14,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZMMDCDYE27NJGVWOATKM5QCJZL","json":"https://pith.science/pith/ZMMDCDYE27NJGVWOATKM5QCJZL.json","graph_json":"https://pith.science/api/pith-number/ZMMDCDYE27NJGVWOATKM5QCJZL/graph.json","events_json":"https://pith.science/api/pith-number/ZMMDCDYE27NJGVWOATKM5QCJZL/events.json","paper":"https://pith.science/paper/ZMMDCDYE"},"agent_actions":{"view_html":"https://pith.science/pith/ZMMDCDYE27NJGVWOATKM5QCJZL","download_json":"https://pith.science/pith/ZMMDCDYE27NJGVWOATKM5QCJZL.json","view_paper":"https://pith.science/paper/ZMMDCDYE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1705.09966&json=true","fetch_graph":"https://pith.science/api/pith-number/ZMMDCDYE27NJGVWOATKM5QCJZL/graph.json","fetch_events":"https://pith.science/api/pith-number/ZMMDCDYE27NJGVWOATKM5QCJZL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZMMDCDYE27NJGVWOATKM5QCJZL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZMMDCDYE27NJGVWOATKM5QCJZL/action/storage_attestation","attest_author":"https://pith.science/pith/ZMMDCDYE27NJGVWOATKM5QCJZL/action/author_attestation","sign_citation":"https://pith.science/pith/ZMMDCDYE27NJGVWOATKM5QCJZL/action/citation_signature","submit_replication":"https://pith.science/pith/ZMMDCDYE27NJGVWOATKM5QCJZL/action/replication_record"}},"created_at":"2026-05-18T00:00:45.326293+00:00","updated_at":"2026-05-18T00:00:45.326293+00:00"}