FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models

A promising class of generative models maps points from a simple distribution a complex distribution through an invertible neural network.-based training of these models requires restricting their to allow cheap computation of Jacobian determinants., the Jacobian trace can be used if the transformation is by an ordinary differential equation. In this paper, we use's trace estimator to give a scalable unbiased estimate of the-density. The result is a continuous-time invertible generative model with density estimation and one-pass sampling, while allowing unrestricted network architectures. We demonstrate our approach on high-dimensional estimation, image generation, and variational inference, achieving the-of-the-art among exact likelihood methods with efficient sampling.