{"id":"W2906729185","doi":"10.1109/tpami.2018.2889948","title":"Group Maximum Differentiation Competition: Model Comparison with Few Samples","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sample (material); Constraint (computer-aided design); Computer science; Set (abstract data type); Artificial intelligence; Quality (philosophy); Space (punctuation); Sample space; Competition (biology); Exploit; Computational model; Machine learning; Perception; Focus (optics); Sample size determination; Computer vision; Mathematics; Statistics; Computer security","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01684831,0.00288043,0.002920711,0.001821029,0.001139529,0.00193871,0.003220428,0.003198695,0.00295113],"category_scores_gemma":[0.03487256,0.0007105156,0.001534928,0.0007562718,0.002988586,0.004206907,0.004592691,0.003337103,0.0004132259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002281177,"about_ca_system_score_gemma":0.002166598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003028271,"about_ca_topic_score_gemma":0.002738796,"domain_scores_codex":[0.9939937,0.003726184,0.0002299823,0.0007968045,0.0009200221,0.0003332916],"domain_scores_gemma":[0.9696227,0.02437418,0.001351632,0.002609943,0.00106185,0.0009795961],"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":[0.00106987,0.0002323908,0.00305109,0.0001872184,0.0002752247,0.0001541889,0.000143154,0.9315303,0.001828744,0.02614175,0.001453029,0.03393298],"study_design_scores_gemma":[0.00004534249,0.0001829604,0.0001717116,0.00001033223,0.00001762645,0.00002699647,0.00001921459,0.9838635,0.0006387862,0.01480965,0.0001987299,0.0000151443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2127307,0.001923977,0.7764406,0.002138213,0.0002068779,0.0003994595,0.0002818284,0.0008795233,0.004998748],"genre_scores_gemma":[0.9071305,0.0001925879,0.08948135,0.0006653942,0.00006327248,0.0002147568,0.0003600068,0.0001398407,0.00175227],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01684831,"threshold_uncertainty_score":0.08910346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02746554561027486,"score_gpt":0.2575376066203482,"score_spread":0.2300720610100733,"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."}}