Fetching the paper…
Reading the bibliography…
Deep generative models based on Generative Adversarial Networks (GANs) have demonstrated impressive sample quality but in order to work they require a careful choice of architecture, parameter initialization, and selection of hyper-parameters.
Training with noise is equivalent to tikhonov regularization
Chris M Bishop · 1995
Earlier work this paper cites.
The effects of adding noise during backpropagation training on a generalization performance
Guozhong An · 1996
Earlier work this paper cites.
Integral probability metrics and their generating classes of functions
Alfred Müller · 1997
Earlier work this paper cites.
Divergence measures and message passing
Tom Minka · 2005
Earlier work this paper cites.
Methods of information geometry
Shun-ichi Amari and Hiroshi Nagaoka · 2007
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
Earlier work this paper cites.
On integral probability metrics, phi-divergences and binary classification
Bharath K Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf, and Gert RG Lanckriet · 2009
Earlier work this paper cites.
Sample complexity of testing the manifold hypothesis
Hariharan Narayanan and Sanjoy Mitter · 2010
Earlier work this paper cites.
Estimating divergence functionals and the likelihood ratio by convex risk minimization
XuanLong Nguyen, Martin J Wainwright, and Michael I Jordan · 2010
Earlier work this paper cites.
Information, divergence and risk for binary experiments
Mark D Reid and Robert C Williamson · 2011
Earlier work this paper cites.
A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
Earlier work this paper cites.
Auto-Encoding Variational Bayes
Diederik P Kingma and Max Welling · 2013
Cited alongside, same era.
Generative Adversarial Networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Stochastic backpropagation and approximate inference in deep generative models
Danilo J Rezende, Shakir Mohamed, and Daan Wierstra · 2014
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Cited alongside, same era.
Generative moment matching networks
Yujia Li, Kevin Swersky, and Richard S Zemel · 2015
Cited alongside, same era.
Disco nets: Dissimilarity coefficients networks
Diane Bouchacourt, Pawan K Mudigonda, and Sebastian Nowozin · 2016
Later among the works it cites.
Mode regularized generative adversarial networks
Tong Che, Yanran Li, Athul Paul Jacob, Yoshua Bengio, and Wenjie Li · 2016
Later among the works it cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Later among the works it cites.
Unrolled generative adversarial networks
Luke Metz, Ben Poole, David Pfau, and Jascha Sohl-Dickstein · 2016
Later among the works it cites.
f-GAN: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
Cited alongside, same era.
Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
Cited alongside, same era.
Multivariate density estimation: theory, practice, and visualization
David W Scott · 2015
Cited alongside, same era.
Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Yinda Zhang, Shuran Song, Ari Seff, and Jianxiong Xiao · 2015
Cited alongside, same era.
Casper Kaae Sønderby, Jose Caballero, Lucas Theis, Wenzhe Shi, and Ferenc Huszár · 2016
Later among the works it cites.
Towards principled methods for training generative adversarial networks
Martin Arjovsky and Léon Bottou · 2017
Closest in time.
Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Closest in time.
Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville · 2017
Closest in time.
The numerics of gans
Lars Mescheder, Sebastian Nowozin, and Andreas Geiger · 2017
Closest in time.