  Limited availability of labeled-data makes any supervised learning problem
challenging. Alternative learning settings like semi-supervised and universum
learning alleviate the dependency on labeled data, but still require a large
amount of unlabeled data, which may be unavailable or expensive to acquire.
GAN-based data generation methods have recently shown promise by generating
synthetic samples to improve learning. However, most existing GAN based
approaches either provide poor discriminator performance under limited labeled
data settings; or results in low quality generated data. In this paper, we
propose a Universum GAN game which provides improved discriminator accuracy
under limited data settings, while generating high quality realistic data. We
further propose an evolving discriminator loss which improves its convergence
and generalization performance. We derive the theoretical guarantees and
provide empirical results in support of our approach.
