{"id":"W3020030698","doi":"10.1109/cvpr42600.2020.00518","title":"Disentangled Image Generation Through Structured Noise Injection","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Noise (video); Generator (circuit theory); Image (mathematics); Code (set theory); Face (sociological concept); Object (grammar); Grid; Artificial intelligence; Generative model; Space (punctuation); Layer (electronics); Computer vision; Pattern recognition (psychology); Generative grammar; Power (physics); Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009970238,0.0003706054,0.0003670247,0.00005713266,0.0001861867,0.0007803873,0.0008397136,0.000223248,0.0001324016],"category_scores_gemma":[0.00006259439,0.000325764,0.0002166868,0.0002615332,0.00004487734,0.0007419712,0.001353611,0.0004130238,0.00006789804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001014151,"about_ca_system_score_gemma":0.0001299157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001945188,"about_ca_topic_score_gemma":0.00007391791,"domain_scores_codex":[0.9978154,0.0002013065,0.0003683301,0.001013272,0.0003378892,0.0002637693],"domain_scores_gemma":[0.9986579,0.00002899096,0.0002215616,0.0008164395,0.0001681602,0.0001069217],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006926495,0.0002552562,0.0001864945,0.0002279464,0.0006907528,0.0001522513,0.005237181,0.1873196,0.4408806,0.05242692,0.2359272,0.07662653],"study_design_scores_gemma":[0.0002635006,0.00006016277,0.0003342268,0.00001789374,0.00004222606,0.000006000788,0.0000275658,0.91504,0.06028777,0.02188306,0.001510786,0.0005268097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001148209,0.0001158852,0.9866443,0.003516425,0.003205474,0.0004633826,0.00001655207,0.0003468398,0.004542944],"genre_scores_gemma":[0.5638685,0.0000552211,0.4325861,0.0009910502,0.002041763,0.00004326126,0.0001221073,0.00002527232,0.0002667544],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7277204,"threshold_uncertainty_score":0.9999194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03524480576759212,"score_gpt":0.2634259380580776,"score_spread":0.2281811322904855,"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."}}