Wasserstein GAN (WGAN) is a type of Generative Adversarial Network (GAN) designed to address issues related to training stability. It employs the Wasserstein distance as a loss function, which measures the difference between the generated data distribution and the real data distribution. This metric provides a smoother and more informative gradient signal, helping to stabilize the training process and improve the quality of generated samples. WGANs are particularly effective in scenarios where traditional GANs struggle with issues like mode collapse or unstable training dynamics.