  Commonly, Deep Neural Networks (DNNs) generalize well on samples drawn from a
distribution similar to that of the training set. However, DNNs' predictions
are brittle and unreliable when the test samples are drawn from a dissimilar
distribution. This presents a major concern for deployment in real-world
applications, where such behavior may come at a great cost -- as in the case of
autonomous vehicles or healthcare applications.
  This paper frames the Out Of Distribution (OOD) detection problem in DNN as a
statistical hypothesis testing problem. Unlike previous OOD detection
heuristics, our framework is guaranteed to maintain the false positive rate
(detecting OOD as in-distribution) for test data. We build on this framework to
suggest a novel OOD procedure based on low-order statistics. Our method
achieves comparable or better than state-of-the-art results on well-accepted
OOD benchmarks without retraining the network parameters -- and at a fraction
of the computational cost.
