{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002409593,0.001784667,0.0009698808,0.0007427555,0.0003706939,0.001238136,0.001681609,0.00148115,0.002836004],"category_scores_gemma":[0.004056657,0.0004800112,0.0007512967,0.0006778322,0.0013673,0.002122167,0.00219387,0.004109984,0.00120238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001345524,"about_ca_system_score_gemma":0.000905324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008929402,"about_ca_topic_score_gemma":0.001362783,"domain_scores_codex":[0.999045,0.0003880868,0.00004479814,0.0001935762,0.000250929,0.00007769949],"domain_scores_gemma":[0.999076,0.0004965707,0.0000846393,0.0001501476,0.0001341621,0.00005843418],"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":[0.00008540847,0.00007683901,0.0005944006,0.0001235078,0.0000614071,0.0001817809,0.00009188254,0.6738524,0.008787245,0.2163322,0.005940143,0.09387283],"study_design_scores_gemma":[0.000006827146,0.00003191534,0.00008971235,0.00001831114,0.000008287802,0.00008703192,0.000005263167,0.9549987,0.001507318,0.04053946,0.002695063,0.0000122519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002044962,0.0002460488,0.9948081,0.0002191772,0.000044963,0.0000392,0.00006308626,0.0002247078,0.002309804],"genre_scores_gemma":[0.4576412,0.001750655,0.5209448,0.0009271844,0.0004229934,0.0005366186,0.001008773,0.0006712922,0.0160965],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002836004,"threshold_uncertainty_score":0.01274329,"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."}}