{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007276831,0.001224576,0.0008020542,0.0004974346,0.0002847858,0.0007401091,0.002046711,0.001444772,0.00408938],"category_scores_gemma":[0.001706344,0.0006546807,0.001131549,0.0004805983,0.0008128145,0.0007987638,0.001737127,0.002188318,0.001391696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008739987,"about_ca_system_score_gemma":0.000662336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0050845,"about_ca_topic_score_gemma":0.009391046,"domain_scores_codex":[0.9997035,0.00009064036,0.000009005897,0.00008246392,0.00008529072,0.00002896125],"domain_scores_gemma":[0.9994786,0.0002882603,0.00004309606,0.0001059827,0.00005413256,0.00002991743],"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":[0.00003970772,0.00002526433,0.000373545,0.0000385332,0.00004528692,0.00005306563,0.00002206475,0.954352,0.001566492,0.008960651,0.004537087,0.0299863],"study_design_scores_gemma":[0.000003476596,0.000005275907,0.00002853777,0.000003843692,0.000002092499,0.00001191921,0.000001314079,0.9954035,0.0003612651,0.003367951,0.0008081204,0.000002626676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009207096,0.0004000412,0.9816381,0.0003232395,0.00007732598,0.00007046515,0.000670219,0.004078693,0.003534797],"genre_scores_gemma":[0.4852936,0.0006779727,0.4928438,0.001330544,0.0001224638,0.0004896228,0.003845573,0.001820612,0.01357585],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0050845,"threshold_uncertainty_score":0.01368034,"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."}}