  The ability of a classifier to recognize unknown inputs is important for many
classification-based systems. We discuss the problem of simultaneous
classification and novelty detection, i.e. determining whether an input is from
the known set of classes and from which specific class, or from an unknown
domain and does not belong to any of the known classes. We propose a method
based on the Generative Adversarial Networks (GAN) framework. We show that a
multi-class discriminator trained with a generator that generates samples from
a mixture of nominal and novel data distributions is the optimal novelty
detector. We approximate that generator with a mixture generator trained with
the Feature Matching loss and empirically show that the proposed method
outperforms conventional methods for novelty detection. Our findings
demonstrate a simple, yet powerful new application of the GAN framework for the
task of novelty detection.
