{"id":"W3122717230","doi":"10.1109/scisisis50064.2020.9322714","title":"Unsupervised Feature Learning for Output Control of Generative Models","year":2020,"lang":"en","type":"article","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México; Global Institution for Collaborative Research and Education, Hokkaido University; Japan Society for the Promotion of Science; Hokkaido University; Telecommunications Advancement Foundation; Institute for Catastrophic Loss Reduction","keywords":"Artificial intelligence; Computer science; Cluster analysis; Generative model; Class (philosophy); Pattern recognition (psychology); Generative grammar; Unsupervised learning; Point (geometry); Feature (linguistics); Data modeling; Machine learning; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001145583,0.0001185438,0.0002380781,0.00002265095,0.00009472162,0.00006111309,0.0003555811,0.00005106321,0.00001122831],"category_scores_gemma":[0.00007969297,0.0000934263,0.0001166235,0.0001590672,0.00002373476,0.0003647272,0.00006699139,0.00009000466,0.000003545417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007172223,"about_ca_system_score_gemma":0.00004122134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006169907,"about_ca_topic_score_gemma":0.00000146352,"domain_scores_codex":[0.999167,0.00008899338,0.0001468322,0.0002930642,0.0001272669,0.0001768184],"domain_scores_gemma":[0.9993708,0.0001378634,0.00006870297,0.0001428214,0.0001886272,0.00009114233],"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":[0.00005002613,0.00002533243,0.00005093518,0.00001741476,0.00009197254,0.00000143599,0.001887987,0.9092283,0.02457358,0.03354253,0.007988795,0.02254168],"study_design_scores_gemma":[0.0007183084,0.0001780319,0.0000180087,0.000003804481,0.00001104461,3.444271e-7,0.00006505995,0.9809661,0.01400804,0.0009186599,0.003002018,0.0001105699],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001772581,0.0001659009,0.9881366,0.009774414,0.00007570749,0.0002689764,0.000004130142,0.00006357296,0.001333379],"genre_scores_gemma":[0.7731927,0.000006995391,0.2244372,0.001732027,0.0001507094,0.00001471067,0.000002344543,0.000006863513,0.0004563882],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7730155,"threshold_uncertainty_score":0.3809814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03299584363253637,"score_gpt":0.2239093068096609,"score_spread":0.1909134631771245,"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."}}