Howtosignalprocessing
What is normalization in GAN?
Spectral Normalization is a weight normalization that stabilizes the training of the discriminator. It controls the Lipschitz constant of the discriminator to mitigate the exploding gradient problem and the mode collapse problem.
What is the major problem with GAN?
Nevertheless, GANs are difficult to train, and training faces two major problems, namely mode collapse, and non-convergence. One feasible method to make GAN solve these two challenges is to redesign the network architecture to get a more powerful model.
What is spectral normalization?
Spectral Normalization is a normalization technique used for generative adversarial networks, used to stabilize training of the discriminator. Spectral normalization has the convenient property that the Lipschitz constant is the only hyper-parameter to be tuned.
