While the core idea of Generative Adversarial Networks (GANs) involves a generator and a discriminator competing in a minimax game, achieving stable and effective training in practice often requires specific techniques. Standard GAN training can suffer from issues like mode collapse, where the generator produces only a limited variety of outputs, or vanishing/exploding gradients, making convergence difficult.

This chapter focuses on understanding these training difficulties and introduces methods designed to improve stability. We will examine:

• The underlying reasons for common training problems.
• Alternative loss functions based on the Wasserstein distance ($W_1$) as implemented in Wasserstein GANs (WGANs).
• Techniques for enforcing the Lipschitz constraint required by WGANs, such as weight clipping and the gradient penalty (WGAN-GP).
• Regularization methods like Spectral Normalization applied to the discriminator.
• Other stabilization approaches like the Two Time-Scale Update Rule (TTUR) and Relativistic GANs.

You will learn the motivation behind these techniques and how they modify the training process to foster better convergence and prevent common failure modes. We will also cover the practical implementation details, preparing you to apply these methods in your own GAN projects.