  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.
