{"id":"W7117511866","doi":"10.1109/inspire67328.2025.11300623","title":"Evaluating Performance of LG-CycleGAN for Photo-Sketch Generation","year":2025,"lang":"","type":"article","venue":"","topic":"Face recognition and analysis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Face (sociological concept); Baseline (sea); Feature (linguistics); Texture (cosmology); Identification (biology); Facial recognition system; Texture synthesis","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001103762,0.0001822311,0.0003235883,0.0003059857,0.0003159967,0.0001929316,0.0004425297,0.00009739775,0.0004925079],"category_scores_gemma":[0.0001490295,0.0001764035,0.0002477237,0.001089882,0.00005554146,0.000499978,0.0001342159,0.00009466041,0.00003091364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005373501,"about_ca_system_score_gemma":0.0003425377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003558292,"about_ca_topic_score_gemma":0.00003725678,"domain_scores_codex":[0.9980776,0.0001011669,0.0006790626,0.000507076,0.0003395887,0.0002954838],"domain_scores_gemma":[0.9984505,0.0001294704,0.0002266545,0.0004374142,0.0006940453,0.00006188716],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002484519,0.0001631419,0.0005392571,0.000355415,0.0001577894,1.496285e-7,0.0003406795,0.00476016,0.08305676,0.006233919,0.0009525899,0.9034153],"study_design_scores_gemma":[0.0005514602,0.0001902755,0.0002089812,0.0001103955,0.00009700211,4.64787e-7,0.00005339234,0.7789893,0.2191483,0.0001848487,0.0003262674,0.0001393199],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4912483,0.0001812093,0.5020317,0.0008602106,0.0004979727,0.0003771465,0.000005642769,0.00003128214,0.004766549],"genre_scores_gemma":[0.9251382,0.0001546554,0.06383426,0.0006167716,0.00008446951,0.00004878598,0.00001222265,0.000006131782,0.0101045],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.903276,"threshold_uncertainty_score":0.7193528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08295414372536128,"score_gpt":0.3633806085330727,"score_spread":0.2804264648077114,"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."}}