{"id":"W2792196892","doi":"10.1109/cvprw.2018.00262","title":"Totally Looks Like - How Humans Compare, Compared to Machines","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Consistency (knowledge bases); Similarity (geometry); Matching (statistics); Artificial intelligence; Convolutional neural network; Set (abstract data type); Code (set theory); Task (project management); Perception; Image (mathematics); Entertainment; Pattern recognition (psychology); Machine learning; Information retrieval; Mathematics","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":[0.002114252,0.0008224424,0.0005588942,0.001156181,0.000440359,0.002194002,0.0009701326,0.001093712,0.0045524],"category_scores_gemma":[0.01614344,0.0002538496,0.0007376654,0.0008915056,0.001585075,0.003510192,0.001586714,0.001263229,0.001846452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006815626,"about_ca_system_score_gemma":0.0003109815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002386431,"about_ca_topic_score_gemma":0.003490826,"domain_scores_codex":[0.996858,0.001330739,0.0001417318,0.0009972043,0.0005316687,0.0001406324],"domain_scores_gemma":[0.9957664,0.001921751,0.0005102644,0.00135336,0.0002602016,0.000188003],"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":[0.00412188,0.0008498669,0.1552333,0.002842775,0.001661859,0.0005998483,0.002618097,0.08771678,0.04971376,0.05644144,0.1141987,0.5240018],"study_design_scores_gemma":[0.0003387319,0.001524432,0.159435,0.0007110192,0.0004548726,0.003225524,0.002675797,0.4529444,0.04815363,0.2168523,0.1132643,0.0004199937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6939777,0.005670663,0.2178887,0.002482298,0.0009151463,0.0006405033,0.03024654,0.004353719,0.04382478],"genre_scores_gemma":[0.9157267,0.000755731,0.05387316,0.0009649957,0.0001308565,0.0002146232,0.02398333,0.0005402045,0.003810315],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0045524,"threshold_uncertainty_score":0.01522928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03056097896151593,"score_gpt":0.2604527930590128,"score_spread":0.2298918140974969,"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."}}