Title: Bounded Information Rate Variational Autoencoders,
Abstract: This paper introduces a new member of the family of Variational Autoencoders
(VAE) that constrains the rate of information transferred by the latent layer.
The latent layer is interpreted as a communication channel, the information
rate of which is bound by imposing a pre-set signal-to-noise ratio. The new
constraint subsumes the mutual information between the input and latent
variables, combining naturally with the likelihood objective of the observed
data as used in a conventional VAE. The resulting Bounded-Information-Rate
Variational Autoencoder (BIR-VAE) provides a meaningful latent representation
with an information resolution that can be specified directly in bits by the
system designer. The rate constraint can be used to prevent overtraining, and
the method naturally facilitates quantisation of the latent variables at the
set rate. Our experiments confirm that the BIR-VAE has a meaningful latent
representation and that its performance is at least as good as state-of-the-art
competing algorithms, but with lower computational complexity.