{"id":"W4322772100","doi":"10.3390/s23052697","title":"Evaluation of GAN-Based Model for Adversarial Training","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adversarial system; Training (meteorology); Computer science; Training set; Artificial intelligence; Engineering; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002570926,0.001223175,0.0007570985,0.000550032,0.0002884989,0.000783594,0.001101686,0.0008867338,0.001765342],"category_scores_gemma":[0.00597042,0.0002293378,0.0004764271,0.0003116012,0.0008241817,0.001117689,0.001109827,0.001522009,0.0003630707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001160578,"about_ca_system_score_gemma":0.0006852369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002878909,"about_ca_topic_score_gemma":0.002137773,"domain_scores_codex":[0.998623,0.0005356197,0.0000537767,0.0001759235,0.0004813389,0.0001303339],"domain_scores_gemma":[0.9972463,0.001678522,0.0001706513,0.0003208429,0.0004683737,0.0001153617],"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":[0.000271817,0.00008001205,0.001216126,0.0001365211,0.00006053882,0.00007747538,0.00002985199,0.9548668,0.004430274,0.005978758,0.00175896,0.03109282],"study_design_scores_gemma":[0.000004789216,0.00006999489,0.0001492045,0.000009090553,0.000005743048,0.00002464867,0.000005314481,0.995915,0.002772065,0.000742034,0.0002967773,0.000005378009],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2381591,0.003237188,0.7328173,0.00139754,0.0005234529,0.0003601757,0.0004591102,0.002652527,0.02039363],"genre_scores_gemma":[0.9506383,0.0005812429,0.04580206,0.0002167224,0.00003185952,0.0001229005,0.0003800897,0.0001188346,0.002107952],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002878909,"threshold_uncertainty_score":0.01359653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1395842735723996,"score_gpt":0.3546751449782767,"score_spread":0.2150908714058771,"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."}}