Machine Learning, Generative Model, Bayesian Machine Learning.
samples from the data distribution it was trained on. However, PixelCNN has a quite different (and, admittedly, fairly image-specific) way of framing it’s training problem. Instead of training on reconstruction loss (like autoencoders) or the criticisms made by an adversarial network (like GANs),