Efficient Training of Unconstrained Injective Flows for Generative Modeling

This paper introduces an efficient training method for injective flows, a type of generative model that jointly learns a low-dimensional data manifold and a distribution on that manifold. The proposed approach, called free-form injective flow (FIF), uses an unconstrained autoencoder architecture and a novel maximum likelihood estimator that avoids the need for restrictive architectural constraints.