{"id":"W4390187158","doi":"10.1109/access.2023.3347498","title":"Knowing is Half the Battle: Enhancing Clean Data Accuracy of Adversarial Robust Deep Neural Networks via Dual-Model Bounded Divergence Gating","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bounded function; Computer science; Divergence (linguistics); Dual (grammatical number); Deep neural networks; Artificial intelligence; Artificial neural network; Gating; Deep learning; 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":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.001763095,0.0003631556,0.0004152735,0.0001754269,0.00101053,0.0006852409,0.007443335,0.0001524639,0.00004030746],"category_scores_gemma":[0.001662275,0.0003114662,0.0001296456,0.001915566,0.0001803429,0.00446532,0.006213625,0.0008345448,0.00002402753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007348219,"about_ca_system_score_gemma":0.0001275226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007260999,"about_ca_topic_score_gemma":0.0002992574,"domain_scores_codex":[0.9961786,0.0003118686,0.0007701066,0.001062508,0.0008362085,0.0008406607],"domain_scores_gemma":[0.994075,0.002533274,0.0006563441,0.00242,0.0001835736,0.0001317904],"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":[0.00001754979,0.0000166369,0.001041887,0.00003065867,0.00004481699,0.00002395067,0.0015656,0.9572916,0.0003555256,0.0003107141,0.0007386425,0.03856239],"study_design_scores_gemma":[0.0003935312,0.00001904297,0.0006974596,0.00006328697,0.0000423088,0.00001003034,0.0001570413,0.9966711,0.0009664421,0.0005767054,0.00005473321,0.0003482992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03871357,0.00006237254,0.954425,0.0008354472,0.005146842,0.0002869486,0.000007906469,0.0003765766,0.0001452816],"genre_scores_gemma":[0.9833519,0.00002537886,0.01523392,0.000320106,0.0009334584,0.00001450407,0.00003133075,0.00004846558,0.00004095489],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9446383,"threshold_uncertainty_score":0.9999337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06996597923306581,"score_gpt":0.3367350883925675,"score_spread":0.2667691091595017,"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."}}