@InProceedings{IshaanGulrajani2017,
author="Ishaan Gulrajani
and Kundan Kumar
and Faruk Ahmed
and Adrien Ali Taiga
and Francesco Visin
and David Vazquez
and Aaron Courville",
title="PixelVAE: A Latent Variable Model for Natural Images",
booktitle="5th International Conference on Learning Representations",
year="2017",
optkeywords="Deep Learning",
optkeywords="Unsupervised Learning",
abstract="Natural image modeling is a landmark challenge of unsupervised learning. Variational Autoencoders (VAEs) learn a useful latent representation and generate samples that preserve global structure but tend to suffer from image blurriness. PixelCNNs model sharp contours and details very well, but lack an explicit latent representation and have difficulty modeling large-scale structure in a computationally efficient way. In this paper, we present PixelVAE, a VAE model with an autoregressive decoder based on PixelCNN. The resulting architecture achieves state-of-the-art log-likelihood on binarized MNIST. We extend PixelVAE to a hierarchy of multiple latent variables at different scales; this hierarchical model achieves competitive likelihood on 64x64 ImageNet and generates high-quality samples on LSUN bedrooms.",
optnote="ADAS; 600.085; 600.076; 601.281; 600.118",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2815), last updated on Thu, 04 Apr 2019 12:38:22 +0200",
opturl="http://104.155.136.4:3000/pdf?id=BJKYvt5lg",
file=":http://refbase.cvc.uab.es/files/gka2016.pdf:PDF"
}