{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013149,0.000225114,0.0003258164,0.0002890856,0.0003859093,0.0002246521,0.0003201915,0.00004920639,0.0001594462],"category_scores_gemma":[0.000001417998,0.000174128,0.0001419197,0.0007445693,0.0001580096,0.0003091135,0.000006658028,0.0001578056,0.00002145242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000250619,"about_ca_system_score_gemma":0.00001184572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004856272,"about_ca_topic_score_gemma":0.004760655,"domain_scores_codex":[0.9985958,0.00009377473,0.0003041173,0.0005222909,0.0002637002,0.0002203455],"domain_scores_gemma":[0.9991602,0.00007241877,0.0001179743,0.00041804,0.0001234238,0.0001079749],"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":[0.00004491537,0.0003822658,0.001549911,0.00001315984,0.0007822286,0.000002663438,0.000726935,0.2543826,0.0006996795,0.001223572,0.00002037011,0.7401717],"study_design_scores_gemma":[0.00009326911,0.000232316,0.001345163,0.00001888701,0.0003064943,0.000002904042,0.00003408753,0.9447091,0.05215899,0.0008486217,0.00002887096,0.0002213229],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003432158,0.00003894631,0.995721,0.0003938887,0.0001296915,0.0001050952,0.0000257212,0.00007097807,0.00008256357],"genre_scores_gemma":[0.9788613,0.00009374278,0.02059855,0.0002985997,0.00004844563,0.0000161215,0.00001182934,0.000009674771,0.00006171021],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9754292,"threshold_uncertainty_score":0.7100733,"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."}}