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 synthetic data generation methods have recently shown promise by
generating synthetic samples to improve task at hand. However, these samples
cannot be used for other purposes. In this paper, we propose a GAN game which
provides improved discriminator accuracy under limited data settings, while
generating realistic synthetic data. This provides the added advantage that now
the generated data can be used for other similar tasks. We provide the
theoretical guarantees and empirical results in support of our approach.Comment: Generative Adversarial Networks, Universum Learning, Semi-Supervised
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