  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.
