{"id":"W3023788528","doi":"10.1016/j.neunet.2020.04.029","title":"Multi-projection of unequal dimension optimal transport theory for Generative Adversary Networks","year":2020,"lang":"en","type":"article","venue":"Neural Networks","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Wasserstein metric; Linear subspace; Dimension (graph theory); Metric (unit); Projection (relational algebra); Generative model; Generative grammar; Mathematical optimization; Parameterized complexity; Differentiable function; Probability distribution; Mathematics; Intrinsic dimension; Artificial intelligence; Algorithm; Applied 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":[0.002900702,0.001807954,0.001734646,0.001387064,0.001036138,0.002116621,0.002025827,0.002769385,0.007143986],"category_scores_gemma":[0.009418727,0.001204341,0.00139628,0.0009309767,0.004524435,0.004561268,0.005633769,0.004763179,0.0007302862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003408865,"about_ca_system_score_gemma":0.001941683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003618459,"about_ca_topic_score_gemma":0.002899803,"domain_scores_codex":[0.9988797,0.0005364163,0.00003609569,0.0002028104,0.0002182261,0.0001267141],"domain_scores_gemma":[0.9947786,0.003693191,0.0003333354,0.0003914745,0.0004558667,0.00034756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005355628,0.00002506833,0.0001747676,0.00007494406,0.0000361306,0.00004574973,0.00007223856,0.3474157,0.0006367509,0.6447817,0.001442485,0.005240962],"study_design_scores_gemma":[0.000005609866,0.00001279358,0.00005190353,0.00001611687,0.000007263598,0.00001610152,0.00001210063,0.7428454,0.0002050518,0.2562928,0.000522571,0.00001234079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01242366,0.0004649328,0.9769849,0.0009379719,0.00007476941,0.00003912453,0.0001453797,0.0001091347,0.008820154],"genre_scores_gemma":[0.8157154,0.001985302,0.1376653,0.0008883318,0.0003100517,0.0004477364,0.0005707866,0.0004906197,0.04192635],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007143986,"threshold_uncertainty_score":0.02473319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02926115820066098,"score_gpt":0.2457177283926195,"score_spread":0.2164565701919586,"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."}}