Variational Bayesian Framework for Advanced Image Generation with
  Domain-Related Variables
Yuxiao Li Santiago Mazuelas Yuan Shen
Deep generative models (DGMs) and their conditional counterparts provide a
powerful ability for general-purpose generative modeling of data distributions.
However, it remains challenging for existing methods to address advanced
conditional generative problems without annotations, which can enable multiple
applications like image-to-image translation and image editing. We present a
unified Bayesian framework for such problems, which introduces an inference
stage on latent variables within the learning process. In particular, we
propose a variational Bayesian image translation network (VBITN) that enables
multiple image translation and editing tasks. Comprehensive experiments show
the effectiveness of our method on unsupervised image-to-image translation, and
demonstrate the novel advanced capabilities for semantic editing and mixed
domain translation.