{"id":"W4365503423","doi":"10.1117/12.2673329","title":"Image rendering efficiency improvement based on deep autoencoder in virtual environment","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Autoencoder; Computer science; Rendering (computer graphics); Artificial intelligence; MNIST database; Cluster analysis; Encoder; Deep learning; Computer vision; Pattern recognition (psychology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005370774,0.0006508528,0.0004962339,0.0004610612,0.0002057318,0.0005964609,0.0006418944,0.0005348264,0.001020631],"category_scores_gemma":[0.001243337,0.0002648747,0.0006566281,0.0003250315,0.0003556932,0.001275884,0.0006303606,0.0009735999,0.0002419771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004989033,"about_ca_system_score_gemma":0.0005438775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00613185,"about_ca_topic_score_gemma":0.005737239,"domain_scores_codex":[0.9997258,0.00005305504,0.00001459079,0.00006762778,0.00009775602,0.00004131718],"domain_scores_gemma":[0.9996769,0.0001134746,0.00002409869,0.00006000701,0.00010482,0.00002069734],"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":[0.0001902051,0.000122173,0.001647279,0.00007994867,0.0001002024,0.0001371145,0.0001100029,0.6493611,0.041274,0.004936669,0.002166151,0.2998751],"study_design_scores_gemma":[0.000003314646,0.00001645472,0.0001705328,0.000001704349,0.000006373654,0.00001888026,0.000004486757,0.9942207,0.004758425,0.0005635475,0.000231721,0.000003864294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07703511,0.0004118478,0.9186573,0.0002123992,0.00008150673,0.00002629724,0.00006479026,0.001327082,0.002183652],"genre_scores_gemma":[0.7174859,0.0005189203,0.2774117,0.0001748106,0.00004288003,0.00003600781,0.0002821348,0.0001980721,0.003849568],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00613185,"threshold_uncertainty_score":0.01219231,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01024954089672091,"score_gpt":0.2485560427183299,"score_spread":0.238306501821609,"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."}}