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

Michael Weiss; Paolo Tonella


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{
  "description": "<p>Modern software systems rely on Deep Neural Networks (DNN) when processing complex, unstructured inputs, such as images, videos, natural language texts or audio signals.&nbsp;Provided the intractably large size of such input spaces, the intrinsic limitations of learning algorithms&nbsp; and the ambiguity about the expected predictions for some of the inputs, not only there is no guarantee that DNN&#39;s predictions are always correct, but rather developers must safely assume a low, though not negligible, error probability.&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.</p>\n\n<p>In this paper, we propose an approach to use DNN uncertainty estimators to implement such supervisor.&nbsp;We first discuss advantages and disadvantages of existing approaches to measure uncertainty for DNNs&nbsp;and propose novel metrics for the empirical assessment of the&nbsp; supervisor that rely on such approaches.&nbsp;We then describe our publicly available tool Uncertainty-Wizard, which allows transparent estimation of uncertainty for regular tf.keras DNNs.&nbsp;Lastly, we discuss a large-scale&nbsp; study conducted on four different subjects to empirically validate the approach,&nbsp;reporting the lessons-learned as guidance for software engineers who intend to monitor uncertainty for fail-safe execution of DLS.</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Universit\u00e0 della Svizzera italiana", 
      "@type": "Person", 
      "name": "Michael Weiss"
    }, 
    {
      "affiliation": "Universit\u00e0 della Svizzera italiana", 
      "@type": "Person", 
      "name": "Paolo Tonella"
    }
  ], 
  "headline": "Fail-Safe Execution of Deep Learning based Systems through Uncertainty Monitoring", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2020-09-01", 
  "url": "https://zenodo.org/record/5055710", 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.5055710", 
  "@id": "https://doi.org/10.5281/zenodo.5055710", 
  "@type": "ScholarlyArticle", 
  "name": "Fail-Safe Execution of Deep Learning based Systems through Uncertainty Monitoring"
}
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