  Variational auto-encoders (VAE) are scalable and powerful generative models.
However, the choice of the variational posterior determines tractability and
flexibility of the VAE. Commonly, latent variables are modeled using the normal
distribution with a diagonal covariance matrix. This results in computational
efficiency but typically it is not flexible enough to match the true posterior
distribution. One fashion of enriching the variational posterior distribution
is application of normalizing flows, i.e., a series of invertible
transformations to latent variables with a simple posterior. In this paper, we
follow this line of thinking and propose a volume-preserving flow that uses a
series of Householder transformations. We show empirically on MNIST dataset and
histopathology data that the proposed flow allows to obtain more flexible
variational posterior and competitive results comparing to other normalizing
flows.
