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Poster
PixelGAN Autoencoders
Alireza Makhzani · Brendan J Frey

Wed Dec 06 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #118
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In this paper, we describe the "PixelGAN autoencoder", a generative autoencoder in which the generative path is a convolutional autoregressive neural network on pixels (PixelCNN) that is conditioned on a latent code, and the recognition path uses a generative adversarial network (GAN) to impose a prior distribution on the latent code. We show that different priors result in different decompositions of information between the latent code and the autoregressive decoder. For example, by imposing a Gaussian distribution as the prior, we can achieve a global vs. local decomposition, or by imposing a categorical distribution as the prior, we can disentangle the style and content information of images in an unsupervised fashion. We further show how the PixelGAN autoencoder with a categorical prior can be directly used in semi-supervised settings and achieve competitive semi-supervised classification results on the MNIST, SVHN and NORB datasets.

Keywords: Semi-Supervised Learning · Deep Learning · Adversarial Networks

Author Information

Alireza Makhzani (University of Toronto)
Brendan J Frey (Deep Genomics, Vector Institute, Univ. Toronto)

Brendan Frey is Co-Founder and CEO of Deep Genomics, a Co-Founder of the Vector Institute for Artificial Intelligence, and a Professor of Engineering and Medicine at the University of Toronto. He is internationally recognized as a leader in machine learning and in genome biology and his group has published over a dozen papers on these topics in Science, Nature and Cell. His work on using deep learning to identify protein-DNA interactions was recently highlighted on the front cover Nature Biotechnology (2015), while his work on deep learning dates back to an early paper on what are now called variational autoencoders (Science 1995). He is a Fellow of the Royal Society of Canada, a Fellow of the Institute for Electrical and Electronic Engineers, and a Fellow of the American Association for the Advancement of Science. He has consulted for several industrial research and development laboratories in Canada, the United States and England, and has served on the Technical Advisory Board of Microsoft Research.

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