{"id":"W4393218562","doi":"10.3390/e26040290","title":"A Unifying Generator Loss Function for Generative Adversarial Networks","year":2024,"lang":"en","type":"article","venue":"Entropy","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Discriminator; MNIST database; Divergence (linguistics); Generator (circuit theory); Function (biology); Mathematics; Applied mathematics; Computer science; Algorithm; Topology (electrical circuits); Artificial neural network; Artificial intelligence; Combinatorics; Power (physics); Physics; Quantum mechanics; Telecommunications","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":[0.0002833063,0.0002137369,0.0001931199,0.00009303709,0.0002907283,0.0005944169,0.0003416676,0.00009654149,0.0000541224],"category_scores_gemma":[0.0000394507,0.0001842535,0.0001830696,0.0003792829,0.0000420863,0.0006676103,0.0001135104,0.0001516695,0.00004731833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008797029,"about_ca_system_score_gemma":0.0001023168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000108928,"about_ca_topic_score_gemma":0.000006198359,"domain_scores_codex":[0.9984842,0.0001111811,0.0002325787,0.0005824364,0.0001816761,0.0004079156],"domain_scores_gemma":[0.9992505,0.0001745091,0.00004947006,0.0003139292,0.0001033349,0.000108207],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001340147,0.00007110121,0.00005416874,0.00003912,0.0004275952,0.00006526701,0.0007014287,0.2144711,0.01593633,0.6019958,0.07646897,0.08963516],"study_design_scores_gemma":[0.0003562657,0.0001328952,0.00002674975,0.00002092371,0.00003717528,0.000004979895,0.00001448136,0.9002447,0.003835673,0.003252507,0.09185704,0.0002166262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005276478,0.00169523,0.989551,0.001047972,0.006335973,0.000340874,0.000009119562,0.0002685497,0.0002236052],"genre_scores_gemma":[0.8790448,0.00007763262,0.1126937,0.00071798,0.006477814,0.000121063,0.00002651488,0.00003502317,0.0008055574],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8785171,"threshold_uncertainty_score":0.751364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01321708750153378,"score_gpt":0.2373218435313356,"score_spread":0.2241047560298018,"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."}}