Spectral normalization

1. What is spectral normalization?
2. Why do we need spectral normalization?
3. What is normalization in Gan?
4. What is conditional Gan?

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.

Why do we need spectral normalization?

Spectral normalization (SN) is a widely-used technique for improving the stability and sample quality of Generative Adversarial Networks (GANs).

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 conditional Gan?

A conditional generative adversarial network (CGAN) is a type of GAN that also takes advantage of labels during the training process. Generator — Given a label and random array as input, this network generates data with the same structure as the training data observations corresponding to the same label.