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Fail-Safe Execution of Deep Learning based Systems through Uncertainty Monitoring

Michael Weiss; Paolo Tonella


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    <subfield code="a">&lt;p&gt;Modern software systems rely on Deep Neural Networks (DNN) when processing complex, unstructured inputs, such as images, videos, natural language texts or audio signals.&amp;nbsp;Provided the intractably large size of such input spaces, the intrinsic limitations of learning algorithms&amp;nbsp; and the ambiguity about the expected predictions for some of the inputs, not only there is no guarantee that DNN&amp;#39;s predictions are always correct, but rather developers must safely assume a low, though not negligible, error probability.&amp;nbsp;A fail-safe Deep Learning based System (DLS) is one equipped to handle DNN faults by means of a supervisor, capable of recognizing predictions that should not be trusted and that should activate a healing procedure bringing the DLS to a safe state.&lt;/p&gt;

&lt;p&gt;In this paper, we propose an approach to use DNN uncertainty estimators to implement such supervisor.&amp;nbsp;We first discuss advantages and disadvantages of existing approaches to measure uncertainty for DNNs&amp;nbsp;and propose novel metrics for the empirical assessment of the&amp;nbsp; supervisor that rely on such approaches.&amp;nbsp;We then describe our publicly available tool Uncertainty-Wizard, which allows transparent estimation of uncertainty for regular tf.keras DNNs.&amp;nbsp;Lastly, we discuss a large-scale&amp;nbsp; study conducted on four different subjects to empirically validate the approach,&amp;nbsp;reporting the lessons-learned as guidance for software engineers who intend to monitor uncertainty for fail-safe execution of DLS.&lt;/p&gt;</subfield>
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