{"id":"W3199806759","doi":"10.1134/s1054661821030123","title":"Identity-Preserved Face Beauty Transformation with Conditional Generative Adversarial Networks","year":2021,"lang":"en","type":"article","venue":"Pattern Recognition and Image Analysis","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Beauty; Normalization (sociology); Computer science; Discriminator; Artificial intelligence; Face (sociological concept); Identity (music); Pattern recognition (psychology); Computer vision; Mathematics; Aesthetics; Art; Linguistics","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":[0.0008464875,0.000813327,0.0004966417,0.0003413071,0.0001985445,0.0003943064,0.0009279064,0.0006065076,0.002104248],"category_scores_gemma":[0.001415364,0.0002832572,0.0006431303,0.0002467162,0.0008442984,0.0006059489,0.001045446,0.001494251,0.0003757222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006686861,"about_ca_system_score_gemma":0.0004105911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001942692,"about_ca_topic_score_gemma":0.001759262,"domain_scores_codex":[0.9996692,0.00008749505,0.00001052587,0.00009393072,0.00009612512,0.00004273026],"domain_scores_gemma":[0.9994925,0.0002604482,0.00006052949,0.0000899353,0.00006745267,0.0000291457],"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":[0.00007262912,0.00003106159,0.000327687,0.00002251504,0.00002488368,0.00004757722,0.00002049075,0.9500961,0.006435636,0.00496921,0.0007301818,0.03722189],"study_design_scores_gemma":[0.00000167716,0.00001000618,0.00003998717,0.000001512376,0.000002969088,0.000008921755,0.000001065407,0.99713,0.001455995,0.001221414,0.0001243105,0.000002214168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03380596,0.0002169514,0.9619786,0.0002128344,0.00006259773,0.00003911606,0.00005704275,0.0007868398,0.002840122],"genre_scores_gemma":[0.8876886,0.0001615097,0.1059779,0.000248168,0.00005364247,0.00007263046,0.0002071768,0.000155659,0.00543471],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002104248,"threshold_uncertainty_score":0.007039428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02052871819918349,"score_gpt":0.2368321409592417,"score_spread":0.2163034227600582,"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."}}