  We extend Generative Adversarial Networks (GANs) to the semi-supervised
context by forcing the discriminator network to output class labels. We train a
generative model G and a discriminator D on a dataset with inputs belonging to
one of N classes. At training time, D is made to predict which of N+1 classes
the input belongs to, where an extra class is added to correspond to the
outputs of G. We show that this method can be used to create a more
data-efficient classifier and that it allows for generating higher quality
samples than a regular GAN.
