TY  - CONF
AU  - Ishaan Gulrajani
AU  - Kundan Kumar
AU  - Faruk Ahmed
AU  - Adrien Ali Taiga
AU  - Francesco Visin
AU  - David Vazquez
AU  - Aaron Courville
A2  - ICLR
PY  - 2017//
TI  - PixelVAE: A Latent Variable Model for Natural Images
BT  - 5th International Conference on Learning Representations
KW  - Deep Learning
KW  - Unsupervised Learning
N2  - 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.
UR  - http://104.155.136.4:3000/pdf?id=BJKYvt5lg
L1  - http://refbase.cvc.uab.es/files/gka2016.pdf
N1  - ADAS; 600.085; 600.076; 601.281; 600.118
ID  - Ishaan Gulrajani2017
ER  -