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Author (up) Ishaan Gulrajani; Kundan Kumar; Faruk Ahmed; Adrien Ali Taiga; Francesco Visin; David Vazquez; Aaron Courville edit   pdf
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  Title PixelVAE: A Latent Variable Model for Natural Images Type Conference Article
  Year 2017 Publication 5th International Conference on Learning Representations Abbreviated Journal  
  Volume Issue Pages  
  Keywords Deep Learning; 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.  
  Address Toulon; France; April 2017  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ICLR  
  Notes ADAS; 600.085; 600.076; 601.281 Approved no  
  Call Number ADAS @ adas @ GKA2017 Serial 2815  
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