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We propose a novel regularizer to improve the training of Generative Adversarial Networks (GANs).
Estimation of the information by an adaptive partitioning of the observation space
Georges A Darbellay and Igor Vajda · 1999
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A new class of entropy estimators for multi-dimensional densities
Erik G Miller · 2003
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High-dimensional entropy estimation for finite accuracy data: R-nn entropy estimator
Jan Kybic · 2007
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Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 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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Approximate all nearest neighbor search for high dimensional entropy estimation for image registration
Jan Kybic and Ivan Vnučko · 2012
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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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On the number of linear regions of deep neural networks
Guido F Montufar, Razvan Pascanu, Kyunghyun Cho, and Yoshua Bengio · 2014
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On the joint entropy of d d -wise-independent variables
Dmitry Gavinsky and Pavel Pudlák · 2015
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Probabilistic backpropagation for scalable learning of bayesian neural networks
José Miguel Hernández-Lobato and Ryan Adams · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Deep multi-scale video prediction beyond mean square error
Michael Mathieu, Camille Couprie, and Yann LeCun · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
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Multivariate density estimation: theory, practice, and visualization
David W Scott · 2015
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On the expressive power of deep neural networks
Maithra Raghu, Ben Poole, Jon Kleinberg, Surya Ganguli, and Jascha Sohl-Dickstein · 2016
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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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Learning from simulated and unsupervised images through adversarial training
Ashish Shrivastava, Tomas Pfister, Oncel Tuzel, Josh Susskind, Wenda Wang, and Russ Webb · 2016
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Energy-based generative adversarial network
Junbo Zhao, Michael Mathieu, and Yann LeCun · 2016
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Generalization and equilibrium in generative adversarial nets (gans)
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Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell · 2016
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Adversarially learned inference
Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Alex Lamb, Martin Arjovsky, Olivier Mastropietro, and Aaron Courville · 2016
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Chelsea Finn, Paul Christiano, Pieter Abbeel, and Sergey Levine · 2016
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Kilian Q Weinberger, and Laurens van der Maaten · 2016
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Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2016
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Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, and Yi Zhang · 2017
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Began: Boundary equilibrium generative adversarial networks
David Berthelot, Tom Schumm, and Luke Metz · 2017
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Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville · 2017
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Distributed bayesian learning with stochastic natural gradient expectation propagation and the posterior server
Leonard Hasenclever, Stefan Webb, Thibaut Lienart, Sebastian Vollmer, Balaji Lakshminarayanan, Charles Blundell, and Yee Whye Teh · 2017
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Peter Henderson, Wei-Di Chang, Pierre-Luc Bacon, David Meger, Joelle Pineau, and Doina Precup · 2017
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Gradient descent gan optimization is locally stable
Vaishnavh Nagarajan and J Zico Kolter · 2017
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