Improved Training of Spectral Normalization Generative Adversarial Networks

Abstract

In order to stabilize the training of generative adversarial networks, several recent works advocate spectral normalization in the discriminator. However, the method ignores the influence of the generator, and the quality of the images generated in practice is unstable. We propose L2 norm regularization in the generator based on the spectral normalization, which can solve the above shortcomings. Our method directly makes the generated data close to real data in Euclidean space, and indirectly helps the spectral normalization achieve tighter Lipschitz constraint during the training of generative adversarial networks. Our experiments on CIFAR-10 and STL-10 dataset confirm that our method can not only stable the quality of the images generated by spectral normalization, but also improve the quality of generated images.