  We propose a simple, efficient, and accurate method for detecting
out-of-distribution (OOD) data for trained neural networks. We propose a novel
descriptor, HAct - activation histograms, for OOD detection, that is,
probability distributions (approximated by histograms) of output values of
neural network layers under the influence of incoming data. We formulate an OOD
detector based on HAct descriptors. We demonstrate that HAct is significantly
more accurate than state-of-the-art in OOD detection on multiple image
classification benchmarks. For instance, our approach achieves a true positive
rate (TPR) of 95% with only 0.03% false-positives using Resnet-50 on standard
OOD benchmarks, outperforming previous state-of-the-art by 20.67% in the false
positive rate (at the same TPR of 95%). The computational efficiency and the
ease of implementation makes HAct suitable for online implementation in
monitoring deployed neural networks in practice at scale.
