  Recent advances in semi-supervised learning with deep generative models have
shown promise in generalizing from small labeled datasets
($\mathbf{x},\mathbf{y}$) to large unlabeled ones ($\mathbf{x}$). In the case
where the codomain has known structure, a large unfeatured dataset
($\mathbf{y}$) is potentially available. We develop a parameter-efficient, deep
semi-supervised generative model for the purpose of exploiting this untapped
data source. Empirical results show improved performance in disentangling
latent variable semantics as well as improved discriminative prediction on
Martian spectroscopic and handwritten digit domains.
