  Efficient sampling of complex data distributions can be achieved using
trained invertible flows (IF), where the model distribution is generated by
pushing a simple base distribution through multiple non-linear bijective
transformations. However, the iterative nature of the transformations in IFs
can limit the approximation to the target distribution. In this paper we seek
to mitigate this by implementing RBM-Flow, an IF model whose base distribution
is a Restricted Boltzmann Machine (RBM) with a continuous smoothing applied. We
show that by using RBM-Flow we are able to improve the quality of samples
generated, quantified by the Inception Scores (IS) and Frechet Inception
Distance (FID), over baseline models with the same IF transformations, but with
less expressive base distributions. Furthermore, we also obtain D-Flow, an IF
model with uncorrelated discrete latent variables. We show that D-Flow achieves
similar likelihoods and FID/IS scores to those of a typical IF with Gaussian
base variables, but with the additional benefit that global features are
meaningfully encoded as discrete labels in the latent space.
