Generative Adversarial, Computer Vision, Gan, Machine Learning, Computer Science.
Chintala and Léon Bottou, use the Wasserstein distance as the objective function, rather than the Jenson-Shannon divergence used in traditional GANs. This objective function is more stable and produces less noisy gradients during training, which can lead to better performance. Additionally, WGANs