good samples and don't get as good of likelihood so here's how you can actually get maximum likelihood out of a generative adversarial network where you approximately minimize the KL divergence between data and model rather than model and data for the discriminator Network you use the same cost function as before which is just the binary classification task and for the generator network we now sample from the generator and then we penalize it according to e to the value of the logits of the discriminator and if the discriminator is optimal this