{"id":"W4406401131","doi":"10.54254/2753-8818/2025.20348","title":"Face Image Generation for Anime Characters based on Generative Adversarial Network","year":2025,"lang":"en","type":"article","venue":"Theoretical and Natural Science","topic":"Face recognition and analysis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Anime; Face (sociological concept); Adversarial system; Generative grammar; Generative adversarial network; Image (mathematics); Computer science; Artificial intelligence; Computer vision; Noma; Linguistics; Telecommunications; Philosophy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004137423,0.000505261,0.0004058037,0.0002906731,0.0001950265,0.0003134015,0.0006273465,0.0005432842,0.002586982],"category_scores_gemma":[0.0009375464,0.0002350554,0.0005244551,0.0001682149,0.0004018498,0.0004384422,0.0006578486,0.0008679677,0.0004656925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003863545,"about_ca_system_score_gemma":0.0002627654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001396495,"about_ca_topic_score_gemma":0.001422868,"domain_scores_codex":[0.9998355,0.00003644348,0.000004465529,0.00004533233,0.00005605942,0.00002224168],"domain_scores_gemma":[0.9997962,0.00009937029,0.00002066437,0.00003319431,0.00003486379,0.00001565733],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001550661,0.00007212507,0.0008868655,0.0000636089,0.00005141678,0.000218285,0.0000882043,0.8194321,0.02706129,0.01186899,0.00376353,0.1363385],"study_design_scores_gemma":[0.000003440874,0.00001213573,0.00005888541,0.000002098496,0.000003025343,0.00003660412,0.000002571849,0.996672,0.00165412,0.001224082,0.000327895,0.000002955577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02712934,0.0002441524,0.9674941,0.0002309533,0.00007651682,0.00006601246,0.00006933502,0.0007182063,0.003971375],"genre_scores_gemma":[0.7819922,0.00031973,0.2088949,0.0003765357,0.00005671231,0.0001524768,0.0002659442,0.0001806931,0.007760813],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002586982,"threshold_uncertainty_score":0.008654356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005964946186638014,"score_gpt":0.2527605137848769,"score_spread":0.2467955675982389,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}