{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007660014,0.000766172,0.0004669282,0.0002191169,0.000245256,0.000841004,0.00103469,0.0008253652,0.005833998],"category_scores_gemma":[0.002270225,0.0003586226,0.0005861983,0.0002388633,0.0009265359,0.00166679,0.001216188,0.002678761,0.002240739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007316947,"about_ca_system_score_gemma":0.0006068575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001856599,"about_ca_topic_score_gemma":0.003571444,"domain_scores_codex":[0.9996716,0.0001034144,0.00001085706,0.0000771432,0.00009677419,0.00004031768],"domain_scores_gemma":[0.9995561,0.0001668166,0.00002848541,0.0001512102,0.00006331474,0.00003409885],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002341329,0.0001257764,0.003148532,0.0001872592,0.0001400996,0.0002527205,0.0001497274,0.491599,0.02234229,0.21684,0.04090374,0.2240766],"study_design_scores_gemma":[0.00002595565,0.00005039285,0.0003807672,0.00003130724,0.00001537904,0.0001615864,0.00001313303,0.9109786,0.007042333,0.06971549,0.01156206,0.00002294612],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01443878,0.0003473314,0.9660301,0.001922289,0.0001768649,0.0000582742,0.000398353,0.003454285,0.01317375],"genre_scores_gemma":[0.6059812,0.0006142043,0.3634104,0.0021883,0.0001469663,0.0002259279,0.001398682,0.00131151,0.02472295],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005833998,"threshold_uncertainty_score":0.01951665,"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."}}