  Auto encoding models have been extensively studied in recent years. They
provide an efficient framework for sample generation, as well as for analysing
feature learning. Furthermore, they are efficient in performing interpolations
between data-points in semantically meaningful ways. In this paper, we build
further on a previously introduced method for generating canonical, dimension
independent, stochastic interpolations. Here, the distribution of interpolation
paths is represented as the distribution of a bridge process constructed from
an artificial random data generating process in the latent space, having the
prior distribution as its invariant distribution. As a result the stochastic
interpolation paths tend to reside in regions of the latent space where the
prior has high mass. This is a desirable feature since, generally, such areas
produce semantically meaningful samples. In this paper, we extend the bridge
process method by introducing a discriminator network that accurately
identifies areas of high latent representation density. The discriminator
network is incorporated as a change of measure of the underlying bridge process
and sampling of interpolation paths is implemented using sequential Monte
Carlo. The resulting sampling procedure allows for greater variability in
interpolation paths and stronger drift towards areas of high data density.
