I am preparing a different series in reinforcement learning. So I am taking a break in writing GAN for now. It is very hard to answer that topic quickly. Fortunately, this article should give you some good information.
Quote from the original paper:
“Our spectral normalization allows the parameter matrix to use as many features as possible while satisfying local 1-Lipschitz constraint”
So it wants to do what WGAN and WGAN-GP wants to achieve. But they doing it with spectra norm in which they also describe a easier way to compute it.