{"id":"W3106068426","doi":"","title":"Your GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent Sampling","year":2020,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Discriminator; Generator (circuit theory); Computer science; Sampling (signal processing); Parameter space; Algorithm; Energy (signal processing); Space (punctuation); Artificial intelligence; Statistics; Mathematics; Physics; Power (physics); Detector; Quantum mechanics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006714512,0.0002173854,0.0002042904,0.00007476029,0.0002278372,0.0002084261,0.0005762955,0.00008546188,0.00001381945],"category_scores_gemma":[0.00001929381,0.0002243651,0.00009524857,0.0003725498,0.0000816644,0.001215262,0.0002104805,0.0001261033,0.000005725849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003918724,"about_ca_system_score_gemma":0.00006884849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001434902,"about_ca_topic_score_gemma":0.00004001285,"domain_scores_codex":[0.9986118,0.00008555609,0.000126566,0.0007855855,0.00008939079,0.0003010714],"domain_scores_gemma":[0.9989768,0.0000474825,0.00008137113,0.0004267086,0.00009233589,0.0003753105],"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":[0.00003537483,0.00004674365,0.00265303,0.000008146444,0.00002979872,0.00006405512,0.0006645914,0.9703342,0.001950519,0.02346433,0.0001633979,0.000585767],"study_design_scores_gemma":[0.000390675,0.00009961762,0.0005433598,0.00001113082,0.00004247022,9.084293e-7,0.00007149926,0.9952845,0.001425257,0.001439168,0.0004044486,0.0002869971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1655452,0.00001301863,0.8333557,0.0007799206,0.00004671734,0.00006616962,0.00001197784,0.0001009223,0.00008039594],"genre_scores_gemma":[0.9839035,0.00003010711,0.01320254,0.002630005,0.00005733073,3.805447e-7,0.0000070026,0.00001640328,0.0001527865],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8201532,"threshold_uncertainty_score":0.9149341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.236810870738154,"score_gpt":0.2153818668001417,"score_spread":0.02142900393801239,"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."}}