of all thousand classes from imagenet and have very good sample diversity the basic idea is to combine adversarial training moment matching in a latent space do you know atom encoders and Monte Carlo sampling using the gradient and the really cool thing is they also work for captioning or inverse captioning where you generate images by giving an input senate's that describes the image overall the basic technique is to follow a Markov chain that moves around in the direction of the gradient of the logarithm of P of x and y with with Y