Well in theory, the adversarial nets should learn to model the distribution perfectly. But there might be a way to do it directly. You could train an NN to prod

Well in theory, the adversarial nets should learn to model the distribution perfectly. But there might be a way to do it directly. You could train an NN to produce samples from a random input source, just like the adversarial nets. But unlike the adversarial nets, the inputs don't need to be random. You could train another NN to predict a distribution of what they should be. And then instead of adversarial training, just regular training to predict the exact pixels, and backproping all the way through.