  Deep generative models have recently yielded encouraging results in producing
subjectively realistic samples of complex data. Far less attention has been
paid to making these generative models interpretable. In many scenarios,
ranging from scientific applications to finance, the observed variables have a
natural grouping. It is often of interest to understand systems of interaction
amongst these groups, and latent factor models (LFMs) are an attractive
approach. However, traditional LFMs are limited by assuming a linear
correlation structure. We present an output interpretable VAE (oi-VAE) for
grouped data that models complex, nonlinear latent-to-observed relationships.
We combine a structured VAE comprised of group-specific generators with a
sparsity-inducing prior. We demonstrate that oi-VAE yields meaningful notions
of interpretability in the analysis of motion capture and MEG data. We further
show that in these situations, the regularization inherent to oi-VAE can
actually lead to improved generalization and learned generative processes.
