UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANs. (arXiv:2201.13279v3 [cs.CV] UPDATED)

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 …

arxiv classifiers gans quantification uncertainty unified model