{"id":"W4375929055","doi":"10.1109/tim.2023.3271746","title":"Few-Shot GAN: Improving the Performance of Intelligent Fault Diagnosis in Severe Data Imbalance","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":100,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Key Research and Development Program of China","keywords":"Overfitting; Sample (material); Computer science; Fault (geology); Margin (machine learning); Artificial intelligence; Machine learning; Offset (computer science); Pattern recognition (psychology); Data mining; Artificial neural network","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":[],"consensus_categories":[],"category_scores_codex":[0.0004849037,0.0001358503,0.0001291982,0.0001716119,0.00007870545,0.00002503033,0.0002051459,0.00004215892,0.00003366033],"category_scores_gemma":[0.00000849254,0.0001159205,0.00002590978,0.0003472773,0.00003195604,0.0002067277,0.000003410958,0.0001744392,0.000009005947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001305929,"about_ca_system_score_gemma":0.00001875993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001240965,"about_ca_topic_score_gemma":0.0004362706,"domain_scores_codex":[0.998935,0.0000342381,0.0003099187,0.0001954089,0.0003603472,0.0001650685],"domain_scores_gemma":[0.9994942,0.00004931308,0.00004312723,0.0003364584,0.00003978676,0.00003713653],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005294376,0.0002460637,0.01362791,0.0005963334,0.0001174269,0.000002035747,0.001752433,0.1235483,0.02525433,0.00001889718,0.001619246,0.833164],"study_design_scores_gemma":[0.0006793659,0.000156545,0.01814881,0.0003092049,0.00004459522,0.000004267052,0.0005873919,0.2774557,0.7013284,0.00001346211,0.001007867,0.0002643109],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9816713,0.00008451334,0.01644188,0.0002476174,0.0004076933,0.0005953069,0.00007536722,0.0002612611,0.0002150152],"genre_scores_gemma":[0.9967552,0.002570368,0.0002471133,0.00005990402,0.00001155001,0.0003139929,0.00001393054,0.00001826924,0.000009711061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8328997,"threshold_uncertainty_score":0.4727099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06045532371034596,"score_gpt":0.292652818747788,"score_spread":0.2321974950374421,"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."}}