  Recent advances in Deep Learning and probabilistic modeling have led to
strong improvements in generative models for images. On the one hand,
Generative Adversarial Networks (GANs) have contributed a highly effective
adversarial learning procedure, but still suffer from stability issues. On the
other hand, Conditional Variational Auto-Encoders (CVAE) models provide a sound
way of conditional modeling but suffer from mode-mixing issues. Therefore,
recent work has turned back to simple and stable regression models that are
effective at generation but give up on the sampling mechanism and the latent
code representation. We propose a novel and efficient stochastic regression
approach with latent drop-out codes that combines the merits of both lines of
research. In addition, a new training objective increases coverage of the
training distribution leading to improvements over the state of the art in
terms of accuracy as well as diversity.
