Infinite Variational Autoencoder for Semi-Supervised Learning

Understand

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.