{"id":"W2765932136","doi":"10.1145/3123266.3123334","title":"Metric-based Generative Adversarial Network","year":2017,"lang":"en","type":"article","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"New York University Abu Dhabi; Canadian Institute for Advanced Research","keywords":"Discriminator; Margin (machine learning); Metric (unit); Generator (circuit theory); Computer science; Artificial intelligence; Sample (material); Feature (linguistics); Feature vector; Generative grammar; Energy (signal processing); Focus (optics); Pattern recognition (psychology); Adversarial system; Machine learning; Mathematics; Power (physics); Statistics; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001032914,0.001345218,0.001003366,0.0004700919,0.0002632978,0.0005442197,0.00123524,0.0008544518,0.002167365],"category_scores_gemma":[0.002817414,0.0003731483,0.0006234251,0.0004258111,0.001096846,0.001172364,0.001413723,0.001776006,0.0004522124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008943698,"about_ca_system_score_gemma":0.0004692383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001089033,"about_ca_topic_score_gemma":0.001422027,"domain_scores_codex":[0.9994137,0.0001912295,0.00002407438,0.0001439606,0.00016698,0.00005994362],"domain_scores_gemma":[0.9990682,0.0005376772,0.00009536356,0.0001456994,0.000109822,0.00004324341],"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":[0.00009105456,0.00002767165,0.0005816118,0.00006116192,0.00004658611,0.00007412361,0.00003462659,0.9217606,0.003647653,0.03047623,0.0018676,0.04133111],"study_design_scores_gemma":[0.000003266822,0.00001325093,0.00005410897,0.000003799832,0.000004831219,0.00002919762,0.000001984926,0.9930699,0.0007180993,0.005668738,0.0004280398,0.000004709439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0117337,0.0004109381,0.9836534,0.0002199654,0.00005312052,0.0000524003,0.0001000112,0.0004012224,0.003375264],"genre_scores_gemma":[0.8447573,0.0005685427,0.1457964,0.0003886544,0.0000711967,0.0002062856,0.0004079828,0.0001734619,0.007630159],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002167365,"threshold_uncertainty_score":0.007250607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02192488872617899,"score_gpt":0.255753912171707,"score_spread":0.233829023445528,"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."}}