Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: UQGAN: A Unified Model for Uncertainty Quantification of Deep
  Classifiers trained via Conditional GANs   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 exhibits 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, 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.

28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
16
The categories of this paper are: stat