{"id":"W4399086070","doi":"10.1111/exsy.13618","title":"Efficient integration of perceptual variational autoencoder into dynamic latent scale generative adversarial network","year":2024,"lang":"en","type":"article","venue":"Expert Systems","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Discriminator; Computer science; Latent variable; Autoencoder; Encoder; Backpropagation; Artificial intelligence; Probabilistic latent semantic analysis; Feature (linguistics); Pattern recognition (psychology); Artificial neural network; Algorithm; Telecommunications","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":[0.0005395747,0.0002302466,0.0003124879,0.0001175783,0.0002005278,0.0002945744,0.0004058773,0.0001162444,0.00004632472],"category_scores_gemma":[0.00003068796,0.0001828034,0.0001566551,0.0004493619,0.00007529807,0.0002716102,0.0001498527,0.0001415781,0.0000596172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000218069,"about_ca_system_score_gemma":0.000167431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004532071,"about_ca_topic_score_gemma":0.00004504872,"domain_scores_codex":[0.9978244,0.0003225888,0.0005066597,0.0005571502,0.000498957,0.0002902718],"domain_scores_gemma":[0.9990889,0.0001636767,0.000111742,0.0003588086,0.0001891461,0.00008771355],"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":[0.00001697795,0.00005418835,0.0000088486,0.00001944449,0.0000918044,0.000007021696,0.01376216,0.9334022,0.01904453,0.02297638,0.005579244,0.005037226],"study_design_scores_gemma":[0.0001656985,0.00007649216,0.0002125375,0.0001448999,0.00001039512,0.000008607373,0.0002793407,0.9962027,0.0006809972,0.000185517,0.001833048,0.0001997702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002502699,0.002072676,0.9867558,0.0006001684,0.007184567,0.0003400901,0.000008650785,0.0001489089,0.0003864683],"genre_scores_gemma":[0.8989999,0.00002346343,0.09904353,0.00009109343,0.001294471,0.00006757004,0.00002087768,0.00001788657,0.000441189],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8964972,"threshold_uncertainty_score":0.7454504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01180601466804709,"score_gpt":0.2510093352804046,"score_spread":0.2392033206123575,"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."}}