contrastive estimation it's clear that the main corner we cut is that we never update the noise distribution and that eliminates a lot of computations right there for generative adversarial networks the way that we're able to cut a corner is that we don't need to make sure that there's an exact correspondence between a density and a sampler so for maximum likelihood we need to be able to sample if we're going to follow this particular implementation of maximum likelihood we need to be able to sample from the model when we