HAct: Out-of-Distribution Detection with Neural Net Activation Histograms

We propose a simple, efficient, and accurate method for detecting out-of-distribution (OOD) data for trained neural networks, a potential first step in methods for OOD generalization. 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 demonstrate that HAct is significantly more accurate than state-of-the-art on multiple OOD image classification benchmarks. For instance, our approach achieves a true positive rate (TPR) of 95% with only 0.05% false-positives using Resnet-50 on standard OOD benchmarks, outperforming previous state-of-the-art by 20.66% in the false positive rate (at the same TPR of 95%). The low computational complexity and the ease of implementation make HAct suitable for online implementation in monitoring deployed neural networks in practice at scale.