{"id":"W3003764885","doi":"10.1109/iccvw.2019.00412","title":"3SGAN: 3D Shape Embedded Generative Adversarial Networks","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; RGB color model; Artificial intelligence; Computer vision; Consistency (knowledge bases); Regularization (linguistics); Enhanced Data Rates for GSM Evolution; Smoothness; Image (mathematics); Generative grammar; Boundary (topology); Algorithm; Pattern recognition (psychology); 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":[],"consensus_categories":[],"category_scores_codex":[0.00008883566,0.0001062995,0.0001218345,0.00004072891,0.00006787205,0.0001097953,0.0004616056,0.00003509537,0.0007195899],"category_scores_gemma":[0.00001003664,0.00008589303,0.00004449752,0.0001738354,0.00001649398,0.0007052866,0.0002606713,0.0001173448,0.0005629037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002035627,"about_ca_system_score_gemma":0.00002733587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003576081,"about_ca_topic_score_gemma":0.000001200843,"domain_scores_codex":[0.9991032,0.00003263652,0.0001320403,0.0003360553,0.0001550729,0.0002409571],"domain_scores_gemma":[0.9993932,0.00004733217,0.0000427596,0.0003885492,0.0000515398,0.00007658885],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002544795,0.00007334308,0.0004232218,0.000004813839,0.00003033714,0.00001995197,0.001036979,0.030642,0.003675061,0.1515496,0.01612226,0.796397],"study_design_scores_gemma":[0.0004076892,0.00003805749,0.0001267919,0.000005394625,0.000001033923,0.000003035442,0.0000232001,0.9857719,0.0005377167,0.0004925607,0.0124562,0.0001364216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001180773,0.00005863406,0.9670271,0.0005205607,0.001450914,0.000118885,1.77464e-7,0.000177731,0.02946522],"genre_scores_gemma":[0.4194763,0.00001994171,0.5666615,0.006646234,0.000335315,0.000004724806,0.000002465041,0.00001243853,0.006841009],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9551299,"threshold_uncertainty_score":0.7879004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008720089667113153,"score_gpt":0.2520833203677399,"score_spread":0.2433632307006268,"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."}}