{"id":"W2965095304","doi":"","title":"Perceptual Generative Autoencoders","year":2019,"lang":"en","type":"article","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; University of Calgary","funders":"","keywords":"Autoencoder; Partitioned global address space; Generative model; Prior probability; Generative grammar; Dimension (graph theory); Intrinsic dimension; Computer science; Artificial intelligence; Space (punctuation); Encoder; Pattern recognition (psychology); Generator (circuit theory); Artificial neural network; Representation (politics); Machine learning; Mathematics; Bayesian probability; Curse of dimensionality; Power (physics)","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":[0.0009561577,0.001057377,0.0008082099,0.0004987248,0.0002366098,0.0008116822,0.001391457,0.0008347167,0.004722032],"category_scores_gemma":[0.002968229,0.0005293937,0.0008606911,0.0004948886,0.001048813,0.001294455,0.001294601,0.001960959,0.001462809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006315377,"about_ca_system_score_gemma":0.0005187925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001740404,"about_ca_topic_score_gemma":0.003160072,"domain_scores_codex":[0.9995665,0.0001263626,0.00001839863,0.0001299881,0.0001149286,0.00004376057],"domain_scores_gemma":[0.9990556,0.0005994206,0.00006170518,0.0001726264,0.00007986279,0.00003078424],"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":[0.00009392813,0.00004750024,0.0005386804,0.0001254035,0.0000985212,0.00008666956,0.00006416298,0.8083125,0.007068194,0.06158598,0.003106761,0.1188718],"study_design_scores_gemma":[0.000005913127,0.00001401711,0.0001027365,0.000009597211,0.000008659549,0.00003669176,0.000004450248,0.9788316,0.001515972,0.01834545,0.001118433,0.000006464114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005882179,0.000322375,0.9899799,0.0001278654,0.00003964058,0.00002798231,0.0001165538,0.0005750717,0.00292851],"genre_scores_gemma":[0.6210803,0.001213718,0.3607023,0.0004942408,0.0001739361,0.000195296,0.001015889,0.0004736099,0.01465064],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004722032,"threshold_uncertainty_score":0.01579678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007993020673644022,"score_gpt":0.2037278601040518,"score_spread":0.1957348394304078,"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."}}