Heatmap-based Out-of-Distribution Detection

TL;DR

This paper introduces a heatmap-based method for out-of-distribution detection that visualizes in- and out-of-distribution regions in images, outperforming prior methods on standard datasets.

Contribution

It proposes a novel heatmap definition for OOD detection based on classifier features, enabling simultaneous detection and visualization of OOD regions.

Findings

Outperforms prior OOD detection methods on CIFAR-10, CIFAR-100, Tiny ImageNet

Provides visual explanations of OOD regions in images

Uses a fixed classifier with a trained decoder for heatmap generation

Abstract

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.