Improving Sequential Latent Variable Models with Autoregressive Flows

Joseph Marino, Lei Chen, Jiawei He, Stephan Mandt

TL;DR: We show how autoregressive flows can be used to improve sequential latent variable models.

Abstract: We propose an approach for sequence modeling based on autoregressive normalizing flows. Each autoregressive transform, acting across time, serves as a moving reference frame for modeling higher-level dynamics. This technique provides a simple, general-purpose method for improving sequence modeling, with connections to existing and classical techniques. We demonstrate the proposed approach both with standalone models, as well as a part of larger sequential latent variable models. Results are presented on three benchmark video datasets, where flow-based dynamics improve log-likelihood performance over baseline models.

Code: https://anonymous.4open.science/r/f02199f7-86d2-45ee-ad23-3f13f769ee10/

Keywords: Autoregressive Flows, Sequence Modeling, Latent Variable Models, Video Modeling, Variational Inference