  We present an approach to quantifying both aleatoric and epistemic
uncertainty for deep neural networks in image classification, based on
generative adversarial networks (GANs). While most works in the literature that
use GANs to generate out-of-distribution (OoD) examples only focus on the
evaluation of OoD detection, we present a GAN based approach to learn a
classifier that produces proper uncertainties for OoD examples as well as for
false positives (FPs). Instead of shielding the entire in-distribution data
with GAN generated OoD examples which is state-of-the-art, we shield each class
separately with out-of-class examples generated by a conditional GAN and
complement this with a one-vs-all image classifier. In our experiments, in
particular on CIFAR10, CIFAR100 and Tiny ImageNet, we improve over the OoD
detection and FP detection performance of state-of-the-art GAN-training based
classifiers. Furthermore, we also find that the generated GAN examples do not
significantly affect the calibration error of our classifier and result in a
significant gain in model accuracy.
