In my previous post about generative adversarial networks [/generative-adversial-networks-explained/], I went over a simple method to training a network that could generate realistic-looking images. However, there were a couple of downsides to using a plain GAN. First, the images are generated off some arbitrary noise. If you wanted to generate a picture with specific features, there's no way of determining which initial noise values would produce that picture, other than searching over the en