One of the challenges in the study of generative adversarial networks is the instability of its training. 
 In this paper, we propose a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator.
 Our new normalization technique is computationally light and easy to incorporate into existing implementations. 
 We tested the efficacy of spectral normalization on CIFAR10, STL-10, and ILSVRC2012 dataset, and we experimentally confirmed that spectrally normalized GANs (SN-GANs) is capable of generating images of better or equal quality relative to the previous training stabilization techniques.
We propose a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator of GANs. This paper uses spectral regularization to normalize GAN objectives, and the ensuing GAN, called SN-GAN, essentially ensures the Lipschitz property of the discriminator. This paper proposes"spectral normalization", moving a nice step forward in improving the training of GANs.