  Generative adversarial networks (GANs) have been remarkably successful in
learning complex high dimensional real word distributions and generating
realistic samples. However, they provide limited control over the generation
process. Conditional GANs (cGANs) provide a mechanism to control the generation
process by conditioning the output on a user defined input. Although training
GANs requires only unsupervised data, training cGANs requires labelled data
which can be very expensive to obtain. We propose a framework for
semi-supervised training of cGANs which utilizes sparse labels to learn the
conditional mapping, and at the same time leverages a large amount of
unsupervised data to learn the unconditional distribution. We demonstrate
effectiveness of our method on multiple datasets and different conditional
tasks.
