Machine Learning, Generative Model, Bayesian Machine Learning.
train on. The Wasserstein GAN is based on a fundamental intuition about using gradient descent to minimize the distance between distributions: you need to have that gradient be informative a lot of the time. When might a gradient become uninformative? As an example, see the attached image. This is