Variational autoencoders
Like DBNs (Chapter 7, Unsupervised Learning) and GANs (see Chapter 9, Generative Models, for more details), variational autoencoders are also generative models. Variational autoencoders (VAEs) are a mix of the best neural networks and Bayesian inference. They are one of the most interesting neural networks and have emerged as one of the most popular approaches to unsupervised learning. They are autoencoders with a twist. Along with the conventional encoder and decoder network of autoencoders, they have additional stochastic layers. The stochastic layer, after the encoder network, samples the data using a Gaussian distribution, and the one after the decoder network samples the data using Bernoulli’s distribution. Like GANs, VAEs can be used to generate images and figures based on the distribution they have been trained on.
VAEs allow one to set complex priors in the latent space and thus learn powerful latent representations. Figure 8.14 describes...
