Wasserstein GAN

Wasserstein GAN (WGAN) is an advanced type of Generative Adversarial Network (GAN) used for generating realistic data, such as images. It consists of two neural networks: a generator that creates data and a discriminator that evaluates it. Unlike traditional GANs, which measure how well the generator produces realistic data through a binary score, WGAN uses a mathematical concept called the Wasserstein distance. This allows for smoother training and better quality of generated data by providing clearer feedback to the generator, reducing issues like mode collapse and unstable training.