other people have argued the generative adversarial networks made good samples and obtained bad likelihood because of the divergence that we chose I no longer believe that and I'm going to give you an argument now that the divergence doesn't matter but I will start by explaining to you why you might think that it should so if we maximize the likelihood of the data that's similar to it that's equivalent to minimizing the KL divergence between the data distribution and the model distribution and that's shown on the