  This paper presents an infinite variational autoencoder (VAE) whose capacity
adapts to suit the input data. This is achieved using a mixture model where the
mixing coefficients are modeled by a Dirichlet process, allowing us to
integrate over the coefficients when performing inference. Critically, this
then allows us to automatically vary the number of autoencoders in the mixture
based on the data. Experiments show the flexibility of our method, particularly
for semi-supervised learning, where only a small number of training samples are
available.
