{"id":"W2751076446","doi":"10.48550/arxiv.1706.00531","title":"PixelGAN Autoencoders","year":2017,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Autoencoder; MNIST database; Computer science; Artificial intelligence; Prior probability; Categorical variable; Autoregressive model; Pattern recognition (psychology); Convolutional neural network; Code (set theory); Path (computing); Generative model; Latent variable; Machine learning; Generative grammar; Artificial neural network; Mathematics; Statistics; Bayesian probability","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":[0.0006230391,0.0009514495,0.0007215009,0.0003815399,0.0002613537,0.0006289848,0.001074706,0.0008336107,0.005180956],"category_scores_gemma":[0.001800246,0.000401044,0.0006448728,0.0003593742,0.000722096,0.001007057,0.001127447,0.001668399,0.002245675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006278409,"about_ca_system_score_gemma":0.0005388447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00201753,"about_ca_topic_score_gemma":0.004849764,"domain_scores_codex":[0.9995965,0.0001001115,0.00001307539,0.0001364367,0.0001088912,0.00004488362],"domain_scores_gemma":[0.9994888,0.0002480394,0.00003948651,0.0001184562,0.00008383229,0.00002133753],"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":[0.0001794298,0.0001116589,0.00135358,0.0002017113,0.0001520723,0.0001568798,0.00009595881,0.5744315,0.01603184,0.05451803,0.01609536,0.3366719],"study_design_scores_gemma":[0.000005927137,0.00001968458,0.000202594,0.00001403473,0.00001108393,0.00006056649,0.000006827182,0.9797885,0.003907142,0.01242567,0.003550677,0.000007302499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01439886,0.0007212389,0.9739031,0.0004058118,0.00012575,0.00005387129,0.0004046918,0.002291088,0.007695555],"genre_scores_gemma":[0.5393475,0.0009669878,0.4265138,0.001029106,0.0001926639,0.0002323702,0.002654971,0.0006347594,0.02842787],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005180956,"threshold_uncertainty_score":0.01733202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07077785933164851,"score_gpt":0.1783267342846297,"score_spread":0.1075488749529812,"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."}}