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Black-box Safety Analysis and Retraining of DNNs based on Feature Extraction and Clustering
[article]
2022
arXiv
pre-print
Deep neural networks (DNNs) have demonstrated superior performance over classical machine learning to support many features in safety-critical systems. Although DNNs are now widely used in such systems (e.g., self driving cars), there is limited progress regarding automated support for functional safety analysis in DNN-based systems. For example, the identification of root causes of errors, to enable both risk analysis and DNN retraining, remains an open problem. In this paper, we propose SAFE,
arXiv:2201.05077v3
fatcat:ucfpcnbm4ngpdlbkoemsxfxor4