Title: Semi-supervised Conditional GANs,
Abstract: We introduce a new model for building conditional generative models in a
semi-supervised setting to conditionally generate data given attributes by
adapting the GAN framework. The proposed semi-supervised GAN (SS-GAN) model
uses a pair of stacked discriminators to learn the marginal distribution of the
data, and the conditional distribution of the attributes given the data
respectively. In the semi-supervised setting, the marginal distribution (which
is often harder to learn) is learned from the labeled + unlabeled data, and the
conditional distribution is learned purely from the labeled data. Our
experimental results demonstrate that this model performs significantly better
compared to existing semi-supervised conditional GAN models.