{"id":"W3155713909","doi":"10.1186/s40537-021-00455-5","title":"Domain randomization for neural network classification","year":2021,"lang":"en","type":"article","venue":"Journal Of Big Data","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Classifier (UML); Convolutional neural network; Artificial intelligence; Artificial neural network; Task (project management); Domain (mathematical analysis); Machine learning; Pattern recognition (psychology); Randomization; Transfer of learning; Contextual image classification; Data mining; Image (mathematics); Clinical trial; Mathematics","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":[0.002752468,0.0007010297,0.0008125714,0.0005964385,0.000454568,0.0008933873,0.001345253,0.001144408,0.00219422],"category_scores_gemma":[0.01188648,0.0003977256,0.0007602952,0.000694101,0.001105957,0.001804967,0.001343205,0.002444146,0.0009318201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001010504,"about_ca_system_score_gemma":0.0008148433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001582684,"about_ca_topic_score_gemma":0.001484896,"domain_scores_codex":[0.9988701,0.0005213528,0.00006033463,0.0003126499,0.0001684736,0.00006700955],"domain_scores_gemma":[0.9967043,0.001784202,0.0002413238,0.0009003299,0.0002763278,0.00009349998],"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":[0.0004214634,0.0002286327,0.003496773,0.0002761554,0.0001362664,0.0001465988,0.00009652542,0.7257523,0.009950066,0.0435123,0.009505554,0.2064774],"study_design_scores_gemma":[0.00001272059,0.00004114962,0.0003314008,0.00001495884,0.000005716495,0.00003651288,0.00001170917,0.9664794,0.002371933,0.02908611,0.001597691,0.00001060522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04178901,0.001329385,0.9510252,0.0006740045,0.0001691504,0.0001305583,0.0005122534,0.002135514,0.002234954],"genre_scores_gemma":[0.659229,0.0008071466,0.3328438,0.0006010117,0.0001943211,0.0006035574,0.002115331,0.0003473205,0.003258478],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002752468,"threshold_uncertainty_score":0.01455659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1690613487970136,"score_gpt":0.3181836121059016,"score_spread":0.149122263308888,"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."}}