Loss Sensitive Generative Adversarial Networks on Lipschitz Densities
In this paper, we present the Lipschitz regularization theory and algorithms for a novel Loss Sensitive Generative Adversarial Network (LS GAN) Specifically, it trains a loss function to distinguish between real and fake samples by designated margins, while learning a generator alternately to produce realistic samples by minimizing their losses. The LS GAN further regularizes its loss function with a Lipschitz regularity condition on the density of real data, yielding a regularized model that can better generalize to produce new data from a reasonable number of training examples than the classic GAN. We will further present a Generalized LS GAN (GLS GAN) and show it contains a large family of regularized GAN models, including both LS GAN and Wasserstein GAN, as its special cases. Compared with the other GAN models, we will conduct experiments to show both LS GAN and GLS GAN exhibit competitive ability in generating new images in terms of the Minimum Reconstruction Error (MRE) assessed on a separate test set. We further extend the LS GAN to a conditional form for supervised and semi supervised learning problems, and demonstrate its outstanding performance on image classification tasks.
International Journal of Computer Vision