Web: http://arxiv.org/abs/2204.00480

Sept. 23, 2022, 1:12 a.m. | Hazem Fahmy, Fabrizio Pastore, Lionel Briand, Thomas Stifter

cs.LG updates on arXiv.org arxiv.org

When Deep Neural Networks (DNNs) are used in safety-critical systems,
engineers should determine the safety risks associated with failures (i.e.,
erroneous outputs) observed during testing. For DNNs processing images,
engineers visually inspect all failure-inducing images to determine common
characteristics among them. Such characteristics correspond to
hazard-triggering events (e.g., low illumination) that are essential inputs for
safety analysis. Though informative, such activity is expensive and

To support such safety analysis practices, we propose SEDE, a technique that
generates readable descriptions …

arxiv debugging dnn events safety safety-critical systems

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