  Deep learning models are known to be overconfident in their predictions on
out of distribution inputs. There have been several pieces of work to address
this issue, including a number of approaches for building Bayesian neural
networks, as well as closely related work on detection of out of distribution
samples. Recently, there has been work on building classifiers that are robust
to out of distribution samples by adding a regularization term that maximizes
the entropy of the classifier output on out of distribution data. To
approximate out of distribution samples (which are not known apriori), a GAN
was used for generation of samples at the edges of the training distribution.
In this paper, we introduce an alternative GAN based approach for building a
robust classifier, where the idea is to use the GAN to explicitly generate out
of distribution samples that the classifier is confident on (low entropy), and
have the classifier maximize the entropy for these samples. We showcase the
effectiveness of our approach relative to state-of-the-art on hand-written
characters as well as on a variety of natural image datasets.
