PixelVAE: A Latent Variable Model for Natural Images
Abstract
Natural image modeling is a landmark challenge of unsupervised learning. Variational Autoencoders (VAEs) learn a useful latent representation and model global structure well but have difficulty capturing small details. PixelCNN models details very well, but lacks a latent code and is difficult to scale for capturing large structures. We present PixelVAE, a VAE model with an autoregressive decoder based on PixelCNN. Our model requires very few expensive autoregressive layers compared to PixelCNN and learns latent codes that are more compressed than a standard VAE while still capturing most non-trivial structure. Finally, we extend our model to a hierarchy of latent variables at different scales. Our model achieves state-of-the-art performance on binarized MNIST, competitive performance on 64x64 ImageNet, and high-quality samples on the LSUN bedrooms dataset.
Cite
@article{arxiv.1611.05013,
title = {PixelVAE: A Latent Variable Model for Natural Images},
author = {Ishaan Gulrajani and Kundan Kumar and Faruk Ahmed and Adrien Ali Taiga and Francesco Visin and David Vazquez and Aaron Courville},
journal= {arXiv preprint arXiv:1611.05013},
year = {2016}
}