In my previous post about generative adversarial networks, 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 entire distribution.
Second, a generative adversarial model only discriminates between “real” and “fake” images. There’s no constraints that an image of a cat has to look like a cat. This leads to results where there’s no actual object in a generated image, but the style just looks like picture.
In this post, I’ll go over the variational autoencoder, a type of network that solves these two problems.