Ordering Dimensions with Nested Dropout Normalizing Flows

Understand

The latent space of normalizing flows must be of the same dimensionality as their output space.

• This constraint presents a problem if we want to learn low-dimensional, semantically meaningful representations.
• Recent work has provided compact representations by fitting flows constrained to manifolds, but hasn't defined a density off that manifold.
• In this work we consider flows with full support in data space, but with ordered latent variables.