Decoupling Global and Local Representations via Invertible Generative Flows

TL;DR

This paper introduces a novel generative model that automatically separates global and local image features using an invertible flow within a VAE framework, achieving effective unsupervised representation learning and image generation.

Contribution

It presents a new architecture combining variational auto-encoders and invertible flows to decouple global and local image representations without supervision.

Findings

Effective density estimation on benchmarks

High-quality image generation results

Unsupervised learning of decoupled representations

Abstract

In this work, we propose a new generative model that is capable of automatically decoupling global and local representations of images in an entirely unsupervised setting, by embedding a generative flow in the VAE framework to model the decoder. Specifically, the proposed model utilizes the variational auto-encoding framework to learn a (low-dimensional) vector of latent variables to capture the global information of an image, which is fed as a conditional input to a flow-based invertible decoder with architecture borrowed from style transfer literature. Experimental results on standard image benchmarks demonstrate the effectiveness of our model in terms of density estimation, image generation and unsupervised representation learning. Importantly, this work demonstrates that with only architectural inductive biases, a generative model with a likelihood-based objective is capable of learning decoupled representations, requiring no explicit supervision. The code for our model is available at https://github.com/XuezheMax/wolf.