  Deep neural networks (DNNs) perform well at classifying inputs associated
with the classes they have been trained on, which are known as in distribution
inputs. However, out-of-distribution (OOD) inputs pose a great challenge to
DNNs and consequently represent a major risk when DNNs are implemented in
safety-critical systems. Extensive research has been performed in the domain of
OOD detection. However, current state-of-the-art methods for OOD detection
suffer from at least one of the following limitations: (1) increased inference
time - this limits existing methods' applicability to many real-world
applications, and (2) the need for OOD training data - such data can be
difficult to acquire and may not be representative enough, thus limiting the
ability of the OOD detector to generalize. In this paper, we propose FOOD --
Fast Out-Of-Distribution detector -- an extended DNN classifier capable of
efficiently detecting OOD samples with minimal inference time overhead. Our
architecture features a DNN with a final Gaussian layer combined with the log
likelihood ratio statistical test and an additional output neuron for OOD
detection. Instead of using real OOD data, we use a novel method to craft
artificial OOD samples from in-distribution data, which are used to train our
OOD detector neuron. We evaluate FOOD's detection performance on the SVHN,
CIFAR-10, and CIFAR-100 datasets. Our results demonstrate that in addition to
achieving state-of-the-art performance, FOOD is fast and applicable to
real-world applications.
