{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001387963,0.0001227849,0.0001254713,0.00005529854,0.0007803994,0.0002820559,0.001735341,0.00005195217,0.00004499693],"category_scores_gemma":[0.00005420278,0.0001283078,0.00009260862,0.0001265167,0.0001387741,0.001249707,0.0004407268,0.0000937347,0.0001593176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003845419,"about_ca_system_score_gemma":0.00004538309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001169176,"about_ca_topic_score_gemma":0.00009006685,"domain_scores_codex":[0.9990993,0.00005218687,0.00007287379,0.0004658522,0.00004834158,0.0002614201],"domain_scores_gemma":[0.9984553,0.00003650546,0.0001190104,0.001201324,0.00006600379,0.0001218411],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002771584,0.0001298499,0.01176102,0.000009792212,0.0001232168,0.0005681184,0.0003605806,0.3329751,0.0007071288,0.6345332,0.006073519,0.01273077],"study_design_scores_gemma":[0.0003430753,0.00003527935,0.007670433,0.000008451916,0.00001321287,0.000002513595,0.00003074855,0.974228,0.000535061,0.01243853,0.004476641,0.000218025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01330194,0.00001084785,0.9635822,0.0004205042,0.0004013633,0.00006411668,0.000001087796,0.0001024734,0.02211545],"genre_scores_gemma":[0.9914165,0.00002695235,0.005152403,0.0001272431,0.00007846621,1.536729e-7,4.71071e-7,0.000006007003,0.003191808],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9781145,"threshold_uncertainty_score":0.6002281,"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."}}