{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006434013,0.0007403176,0.0005112893,0.000345983,0.0001485607,0.0004854852,0.001077968,0.0009427821,0.003545467],"category_scores_gemma":[0.00162995,0.0003775866,0.0003960923,0.0003943545,0.0004266534,0.0006775564,0.001070176,0.001683802,0.001358413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003788712,"about_ca_system_score_gemma":0.0003696733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001605163,"about_ca_topic_score_gemma":0.003338658,"domain_scores_codex":[0.9997241,0.00008415164,0.000006847599,0.00006621314,0.00009398364,0.00002476024],"domain_scores_gemma":[0.9996402,0.0001886951,0.00002325118,0.0000730407,0.00006079758,0.00001403638],"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":[0.0001486791,0.00007092238,0.0004618032,0.0001007486,0.0001078672,0.000141414,0.00003314584,0.6654865,0.01946388,0.03897047,0.01899564,0.2560188],"study_design_scores_gemma":[0.000005041471,0.00001121447,0.00006925827,0.000004772877,0.000005484094,0.00003068664,0.000001522246,0.9878656,0.002977251,0.007107833,0.001917064,0.000004257085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003347199,0.0002266777,0.9913949,0.000140864,0.00007759137,0.00001823963,0.000137079,0.00187619,0.002781301],"genre_scores_gemma":[0.386232,0.0006086254,0.5832995,0.0008656026,0.0001926452,0.000174155,0.00129162,0.001328648,0.02600723],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003545467,"threshold_uncertainty_score":0.01186073,"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."}}