{"id":"W3204276937","doi":"10.1109/icas49788.2021.9551148","title":"Thermal Face Image Generator","year":2021,"lang":"en","type":"article","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Image translation; Image (mathematics); Generator (circuit theory); Face (sociological concept); Computer science; Task (project management); Artificial intelligence; Translation (biology); Computer vision; Thermal; Facial recognition system; Measure (data warehouse); Pattern recognition (psychology); Data mining; Engineering; Power (physics); Geography","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":[0.0006709996,0.0009119531,0.0005848556,0.0004431373,0.000222024,0.0005321453,0.001309516,0.0008167431,0.01312411],"category_scores_gemma":[0.001756765,0.0003233067,0.0007637787,0.0002745952,0.0004379581,0.0007310225,0.0008995021,0.001301547,0.004841415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004592422,"about_ca_system_score_gemma":0.0004207068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009823196,"about_ca_topic_score_gemma":0.001264018,"domain_scores_codex":[0.9996634,0.00004804035,0.000008175131,0.0001295166,0.0001068829,0.00004395174],"domain_scores_gemma":[0.9996779,0.00008123525,0.00002024907,0.0001199749,0.00008266795,0.00001795882],"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":[0.0007104494,0.0002772434,0.001676366,0.0003622859,0.0001434223,0.0003920669,0.00009464107,0.3151899,0.08437415,0.01927854,0.04612627,0.5313746],"study_design_scores_gemma":[0.00003637924,0.0001155054,0.0007488499,0.00002140523,0.00002480354,0.0004031771,0.00001774413,0.9396438,0.04456041,0.005777758,0.008623683,0.00002653027],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03934235,0.0007639302,0.932607,0.0004146226,0.0006437353,0.0004623284,0.001476668,0.008680135,0.01560934],"genre_scores_gemma":[0.5478676,0.0005396692,0.4086827,0.0009667006,0.0002004186,0.0007480159,0.004982464,0.001185572,0.03482688],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01312411,"threshold_uncertainty_score":0.04390454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01176294166115662,"score_gpt":0.2201904548640293,"score_spread":0.2084275132028727,"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."}}