Title: Class-Splitting Generative Adversarial Networks,
Abstract: Generative Adversarial Networks (GANs) produce systematically better quality
samples when class label information is provided., i.e. in the conditional GAN
setup. This is still observed for the recently proposed Wasserstein GAN
formulation which stabilized adversarial training and allows considering high
capacity network architectures such as ResNet. In this work we show how to
boost conditional GAN by augmenting available class labels. The new classes
come from clustering in the representation space learned by the same GAN model.
The proposed strategy is also feasible when no class information is available,
i.e. in the unsupervised setup. Our generated samples reach state-of-the-art
Inception scores for CIFAR-10 and STL-10 datasets in both supervised and
unsupervised setup.