Generative Adversarial Networks can produce impressive results, but their training process is often unstable. Achieving convergence between the generator $G$ and discriminator $D$ in the min-max objective $\min_G \max_D V(D, G)$ is frequently difficult, leading to problems like poor sample quality or lack of diversity.

This chapter provides practical methods to diagnose and address these training issues. You will learn to:

• Identify common problems such as mode collapse, training oscillations, and divergence.
• Implement alternative loss functions like Wasserstein GAN with Gradient Penalty (WGAN-GP) and Least Squares GAN (LSGAN) to improve stability.
• Apply regularization techniques, including spectral normalization, to the generator and discriminator.
• Utilize optimization strategies like the Two Time-Scale Update Rule (TTUR).
• Develop effective hyperparameter tuning approaches for GANs.

We will cover the reasoning behind these techniques and guide you through implementing key solutions like WGAN-GP.