Generative Adversarial, Computer Vision, Gan, Machine Learning, Computer Science.
the two distributions. Additionally, the Wasserstein distance is more smooth and has less local optima which makes the training process more stable. Another important difference between GANs and WGANs is the use of weight clipping to enforce the Lipschitz constraint of the discriminator network. In