WGANs

WGANs, or Wasserstein Generative Adversarial Networks, are a type of generative model designed to address some of the training stability issues common in traditional Generative Adversarial Networks (GANs). The core innovation of WGANs lies in their use of the Wasserstein distance, also known as the Earth Mover's distance, as the loss function. This distance metric provides a more stable and meaningful measure of the difference between the real data distribution and the generated data distribution compared to the Jensen-Shannon divergence used in standard GANs.

Traditional GANs often suffer from vanishing gradients, where the discriminator becomes too good too quickly, preventing the generator from learning. The Wasserstein distance, on the other hand, is continuous and differentiable even when the two distributions have no overlap, leading to more informative gradients for the generator. This allows the generator to learn more effectively, even in challenging scenarios.

To implement the Wasserstein distance, WGANs replace the discriminator with a critic. The critic's role is to estimate the Wasserstein distance, and it is trained to satisfy the Lipschitz constraint, a mathematical condition that ensures the gradients remain bounded. This constraint is typically enforced through gradient penalty or weight clipping. The improved stability and more meaningful gradients provided by the Wasserstein distance make WGANs a powerful tool for generating high-quality synthetic data in various applications.