{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006082971,0.0006507356,0.0005438391,0.0002786687,0.0001534885,0.0003953955,0.0008135449,0.0005114965,0.002192874],"category_scores_gemma":[0.001150225,0.0003608096,0.0004855127,0.0002510245,0.0005085078,0.0005833447,0.0009450259,0.001118936,0.0004095415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004865475,"about_ca_system_score_gemma":0.0004342895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002493366,"about_ca_topic_score_gemma":0.003614888,"domain_scores_codex":[0.9997556,0.00006970125,0.000008339685,0.00005609264,0.00008140706,0.00002888646],"domain_scores_gemma":[0.9997357,0.0001404674,0.00001992546,0.00003562865,0.00005164245,0.00001670053],"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":[0.00004684298,0.00002610758,0.0002621607,0.00003402882,0.00003456051,0.00004891153,0.00002384365,0.916983,0.007218296,0.01138419,0.001275183,0.06266296],"study_design_scores_gemma":[9.123336e-7,0.000004491362,0.00001612974,0.000001066657,0.000001264402,0.000005195984,7.335479e-7,0.9985657,0.0004220031,0.0008318337,0.0001496258,0.000001100898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006474227,0.0001215587,0.9912575,0.00007194316,0.00002416281,0.00001763458,0.00002397386,0.0003826169,0.00162636],"genre_scores_gemma":[0.7188506,0.0002402898,0.2732432,0.0002427148,0.00005384018,0.00009879821,0.0002078476,0.0002108839,0.006851929],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002493366,"threshold_uncertainty_score":0.007335901,"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."}}