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In this paper, we propose the "adversarial autoencoder" (AAE), which is a probabilistic autoencoder that uses the recently proposed generative adversarial networks (GAN) to perform variational inference by matching the aggregated posterior of the hidden code vector of the autoencoder with an arbitrary prior distribution.
A fast learning algorithm for deep belief nets
Geoffrey E. Hinton, Simon Osindero, and Yee Whye Teh · 2006
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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Deep boltzmann machines
Ruslan Salakhutdinov and Geoffrey E Hinton · 2009
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Learning a parametric embedding by preserving local structure
Laurens Maaten · 2009
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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Better mixing via deep representations
Yoshua Bengio, Grégoire Mesnil, Yann Dauphin, and Salah Rifai · 2013
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
Cited alongside, same era.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Cited alongside, same era.
Deep generative stochastic networks trainable by backprop
Yoshua Bengio, Eric Thibodeau-Laufer, Guillaume Alain, and Jason Yosinski · 2014
Cited alongside, same era.
Semi-supervised learning with deep generative models
Diederik P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling · 2014
Cited alongside, same era.
Importance weighted autoencoders
Yuri Burda, Roger Grosse, and Ruslan Salakhutdinov · 2015
Cited alongside, same era.
Generative moment matching networks
Distributional smoothing with virtual adversarial training
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, and Shin Ishii · 2015
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Unsupervised and semi-supervised learning with categorical generative adversarial networks
Jost Tobias Springenberg · 2015
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Semi-supervised learning with ladder networks
Antti Rasmus, Mathias Berglund, Mikko Honkala, Harri Valpola, and Tapani Raiko · 2015
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Yujia Li, Kevin Swersky, and Richard Zemel · 2015
Cited alongside, same era.
A note on the evaluation of generative models
Lucas Theis, Aäron van den Oord, and Matthias Bethge · 2015
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Non-linear dimensionality reduction
Geoffrey Hinton
Cited in the paper.
Sergey Ioffe and Christian Szegedy · 2015
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Auxiliary deep generative models
Lars Maaløe, Casper Kaae Sønderby, Søren Kaae Sønderby, and Ole Winther · 2016
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