Reviews: Co-Generation with GANs using AIS based HMC

This is a purely empirical study that considers a problem of co-generation in the context of deep unsupervised generative models. Given a part of the example is observed, one is required to fill in the remaining (unobserved) part in a reasonable way. The problem is well motivated by applications such as image in-painting. The authors provide an extensive overview of the existing literature. The proposed solution is simple and uses an already trained GAN generator G: Z \to X to find latent vectors z resulting in outputs G(z) looking similar to the observed part of the image.