Normalizing flow neural networks by JKO scheme

Understand

Normalizing flow is a class of deep generative models for efficient sampling and likelihood estimation, which achieves attractive performance, particularly in high dimensions.

• The flow is often implemented using a sequence of invertible residual blocks.
• Existing works adopt special network architectures and regularization of flow trajectories.
• In this paper, we develop a neural ODE flow network called JKO-iFlow, inspired by the Jordan-Kinderleherer-Otto (JKO) scheme, which unfolds the discrete-time dynamic of the Wasserstein gradient flow.