{"id":"W3197235162","doi":"10.1007/s10489-022-03199-8","title":"VARGAN: variance enforcing network enhanced GAN","year":2022,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Generator (circuit theory); Variance (accounting); Set (abstract data type); Convergence (economics); Mode (computer interface); Process (computing); Modality (human–computer interaction); Modal; Generative grammar; Network architecture; Artificial intelligence; Diversity (politics); Image (mathematics); Machine learning; Algorithm; Pattern recognition (psychology); Human–computer interaction","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":[0.0005903945,0.0002225784,0.0002405256,0.00005781175,0.0008397833,0.0001546032,0.001649706,0.00003901212,0.0005587491],"category_scores_gemma":[0.00002544634,0.0002351436,0.00008029671,0.001035883,0.00005764068,0.0002584302,0.0007214815,0.0003374752,0.0001679875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009399241,"about_ca_system_score_gemma":0.00008701409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002742557,"about_ca_topic_score_gemma":0.000004302399,"domain_scores_codex":[0.9978788,0.0001014057,0.0003516923,0.0006673082,0.0004061976,0.0005946025],"domain_scores_gemma":[0.9987122,0.0002020522,0.0001605692,0.0007632853,0.00005306042,0.000108864],"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":[0.00001473398,0.00003971666,0.000006704353,0.000003797494,0.00002444065,0.000007792499,0.0007965723,0.6218188,0.009312078,0.248677,0.002414235,0.1168841],"study_design_scores_gemma":[0.000216254,0.0002327566,0.0001361278,0.00001972832,0.00002205938,0.0000276964,0.0005271091,0.5416703,0.2873844,0.08928721,0.07932895,0.001147421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001780186,0.0001712415,0.9611344,0.0002328829,0.0009438818,0.0002577937,0.000001783533,0.0001786168,0.03690143],"genre_scores_gemma":[0.9195335,0.00003450757,0.07806271,0.001368772,0.0003421833,0.000162445,0.000003725856,0.00001795332,0.0004742018],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9193555,"threshold_uncertainty_score":0.9588876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0114793609412819,"score_gpt":0.2138167925107513,"score_spread":0.2023374315694693,"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."}}