{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:42C55Y4637PCUCH3DDJBBXN2AU","short_pith_number":"pith:42C55Y46","schema_version":"1.0","canonical_sha256":"e685dee39edfde2a08fb18d210ddba05076e02b4601f96d137efc46bfdfb34b2","source":{"kind":"arxiv","id":"2204.11887","version":2},"attestation_state":"computed","paper":{"title":"Evolutionary latent space search for driving human portrait generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.NE"],"primary_cat":"cs.CV","authors_text":"Benjam\\'in Mach\\'in, Jamal Toutouh, Sergio Nesmachnow","submitted_at":"2022-04-25T18:00:49Z","abstract_excerpt":"This article presents an evolutionary approach for synthetic human portraits generation based on the latent space exploration of a generative adversarial network. The idea is to produce different human face images very similar to a given target portrait. The approach applies StyleGAN2 for portrait generation and FaceNet for face similarity evaluation. The evolutionary search is based on exploring the real-coded latent space of StyleGAN2. The main results over both synthetic and real images indicate that the proposed approach generates accurate and diverse solutions, which represent realistic h"},"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":"2204.11887","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-04-25T18:00:49Z","cross_cats_sorted":["cs.AI","cs.LG","cs.NE"],"title_canon_sha256":"544d0edac53e95062889526cd8fd455ac05e9a5be04420dabe030b893ad44c3d","abstract_canon_sha256":"0f7977c8176025c86eb9d3561773fe2549537795c7e1df05a2b2292cc3e1798d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:24:40.413689Z","signature_b64":"ZZC8jrWiwHoN+6hRu2l49k3zCQ4vShitU6MOTP/fW02PmKOASUNIjg5jlYwEvoZPwGTni6Xir8uzn5BGx8xWAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e685dee39edfde2a08fb18d210ddba05076e02b4601f96d137efc46bfdfb34b2","last_reissued_at":"2026-07-05T04:24:40.413248Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:24:40.413248Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Evolutionary latent space search for driving human portrait generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.NE"],"primary_cat":"cs.CV","authors_text":"Benjam\\'in Mach\\'in, Jamal Toutouh, Sergio Nesmachnow","submitted_at":"2022-04-25T18:00:49Z","abstract_excerpt":"This article presents an evolutionary approach for synthetic human portraits generation based on the latent space exploration of a generative adversarial network. The idea is to produce different human face images very similar to a given target portrait. The approach applies StyleGAN2 for portrait generation and FaceNet for face similarity evaluation. The evolutionary search is based on exploring the real-coded latent space of StyleGAN2. The main results over both synthetic and real images indicate that the proposed approach generates accurate and diverse solutions, which represent realistic h"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.11887","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2204.11887/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2204.11887","created_at":"2026-07-05T04:24:40.413305+00:00"},{"alias_kind":"arxiv_version","alias_value":"2204.11887v2","created_at":"2026-07-05T04:24:40.413305+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.11887","created_at":"2026-07-05T04:24:40.413305+00:00"},{"alias_kind":"pith_short_12","alias_value":"42C55Y4637PC","created_at":"2026-07-05T04:24:40.413305+00:00"},{"alias_kind":"pith_short_16","alias_value":"42C55Y4637PCUCH3","created_at":"2026-07-05T04:24:40.413305+00:00"},{"alias_kind":"pith_short_8","alias_value":"42C55Y46","created_at":"2026-07-05T04:24:40.413305+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.05863","citing_title":"Evolutionary ecology of words","ref_index":10,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/42C55Y4637PCUCH3DDJBBXN2AU","json":"https://pith.science/pith/42C55Y4637PCUCH3DDJBBXN2AU.json","graph_json":"https://pith.science/api/pith-number/42C55Y4637PCUCH3DDJBBXN2AU/graph.json","events_json":"https://pith.science/api/pith-number/42C55Y4637PCUCH3DDJBBXN2AU/events.json","paper":"https://pith.science/paper/42C55Y46"},"agent_actions":{"view_html":"https://pith.science/pith/42C55Y4637PCUCH3DDJBBXN2AU","download_json":"https://pith.science/pith/42C55Y4637PCUCH3DDJBBXN2AU.json","view_paper":"https://pith.science/paper/42C55Y46","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2204.11887&json=true","fetch_graph":"https://pith.science/api/pith-number/42C55Y4637PCUCH3DDJBBXN2AU/graph.json","fetch_events":"https://pith.science/api/pith-number/42C55Y4637PCUCH3DDJBBXN2AU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/42C55Y4637PCUCH3DDJBBXN2AU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/42C55Y4637PCUCH3DDJBBXN2AU/action/storage_attestation","attest_author":"https://pith.science/pith/42C55Y4637PCUCH3DDJBBXN2AU/action/author_attestation","sign_citation":"https://pith.science/pith/42C55Y4637PCUCH3DDJBBXN2AU/action/citation_signature","submit_replication":"https://pith.science/pith/42C55Y4637PCUCH3DDJBBXN2AU/action/replication_record"}},"created_at":"2026-07-05T04:24:40.413305+00:00","updated_at":"2026-07-05T04:24:40.413305+00:00"}