Decoupling Global and Local Representations via Invertible Generative Flows
This work addresses the challenge of learning disentangled representations without explicit supervision, which is important for improving interpretability and control in generative models, though it is incremental as it builds on existing VAE and flow-based methods.
The authors tackled the problem of unsupervised decoupling of global and local image representations by proposing a generative model that combines a VAE with an invertible flow-based decoder, achieving effective results in density estimation, image generation, and representation learning on standard benchmarks.
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.