  Our work investigates out-of-distribution (OOD) detection as a neural network
output explanation problem. We learn a heatmap representation for detecting OOD
images while visualizing in- and out-of-distribution image regions at the same
time. Given a trained and fixed classifier, we train a decoder neural network
to produce heatmaps with zero response for in-distribution samples and high
response heatmaps for OOD samples, based on the classifier features and the
class prediction. Our main innovation lies in the heatmap definition for an OOD
sample, as the normalized difference from the closest in-distribution sample.
The heatmap serves as a margin to distinguish between in- and
out-of-distribution samples. Our approach generates the heatmaps not only for
OOD detection, but also to indicate in- and out-of-distribution regions of the
input image. In our evaluations, our approach mostly outperforms the prior work
on fixed classifiers, trained on CIFAR-10, CIFAR-100 and Tiny ImageNet. The
code is publicly available at: https://github.com/jhornauer/heatmap_ood.
