Joint Manifold Learning and Density Estimation Using Normalizing Flows

TL;DR

This paper introduces joint manifold learning and density estimation methods using normalizing flows, addressing their limitations in capturing data manifolds and improving likelihood estimation for high-dimensional data.

Contribution

The paper proposes a novel single-step approach with per-pixel penalized likelihood and hierarchical training to disentangle manifold and off-manifold parts in normalizing flows.

Findings

Improved image quality in generated samples.

Enhanced likelihood estimation on data sub-manifolds.

Effective joint manifold learning and density estimation.

Abstract

Based on the manifold hypothesis, real-world data often lie on a low-dimensional manifold, while normalizing flows as a likelihood-based generative model are incapable of finding this manifold due to their structural constraints. So, one interesting question arises: $\textit{"Can we find sub-manifold(s) of data in normalizing flows and estimate the density of the data on the sub-manifold(s)?"}$. In this paper, we introduce two approaches, namely per-pixel penalized log-likelihood and hierarchical training, to answer the mentioned question. We propose a single-step method for joint manifold learning and density estimation by disentangling the transformed space obtained by normalizing flows to manifold and off-manifold parts. This is done by a per-pixel penalized likelihood function for learning a sub-manifold of the data. Normalizing flows assume the transformed data is Gaussianizationed, but this imposed assumption is not necessarily true, especially in high dimensions. To tackle this problem, a hierarchical training approach is employed to improve the density estimation on the sub-manifold. The results validate the superiority of the proposed methods in simultaneous manifold learning and density estimation using normalizing flows in terms of generated image quality and likelihood.