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This work explores conditional image generation with a new image density model based on the PixelCNN architecture.
Offline handwriting recognition with multidimensional recurrent neural networks
Alex Graves and Jürgen Schmidhuber · 2009
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The neural autoregressive distribution estimator
Hugo Larochelle and Iain Murray · 2011
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Stacked convolutional auto-encoders for hierarchical feature extraction
Jonathan Masci, Ueli Meier, Dan Cireşan, and Jürgen Schmidhuber · 2011
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Learning with hierarchical-deep models
Ruslan Salakhutdinov, Joshua B Tenenbaum, and Antonio Torralba · 2013
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NICE: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Deep autoregressive networks
Karol Gregor, Ivo Danihelka, Andriy Mnih, Charles Blundell, and Daan Wierstra · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo J Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Factoring variations in natural images with deep gaussian mixture models
Aäron van den Oord and Benjamin Schrauwen · 2014
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The student-t mixture as a natural image patch prior with application to image compression
Aaron van den Oord and Benjamin Schrauwen · 2014
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Deep generative image models using a laplacian pyramid of adversarial networks
Emily L Denton, Soumith Chintala, Rob Fergus, et al · 2015
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A neural algorithm of artistic style
Leon A Gatys, Alexander S Ecker, and Matthias Bethge · 2015
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DRAW: A recurrent neural network for image generation
Karol Gregor, Ivo Danihelka, Alex Graves, and Daan Wierstra · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Łukasz Kaiser and Ilya Sutskever · 2015
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Nal Kalchbrenner, Ivo Danihelka, and Alex Graves · 2015
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Generative image modeling using spatial LSTMs
Lucas Theis and Matthias Bethge · 2015
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A note on the evaluation of generative models
Lucas Theis, Aaron van den Oord, and Matthias Bethge · 2015
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Locally-connected transformations for deep gmms
Aaron van den Oord and Joni Dambre · 2015
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems
Martın Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al · 2016
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Unifying count-based exploration and intrinsic motivation
Marc G Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, and Remi Munos · 2016
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Action-conditional video prediction using deep networks in atari games
Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard L Lewis, and Satinder Singh · 2015
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Inceptionism: Going deeper into neural networks
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Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Training very deep networks
Rupesh K Srivastava, Klaus Greff, and Jürgen Schmidhuber · 2015
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Towards conceptual compression
Karol Gregor, Frederic Besse, Danilo J Rezende, Ivo Danihelka, and Daan Wierstra · 2016
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Generative adversarial text to image synthesis
Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, and Honglak Lee · 2016
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One-shot generalization in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, Ivo Danihelka, Karol Gregor, and Daan Wierstra · 2016
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Neural autoregressive distribution estimation
Benigno Uria, Marc-Alexandre Côté, Karol Gregor, Iain Murray, and Hugo Larochelle · 2016
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Pixel recurrent neural networks
Aaron van den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
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