Fetching the paper…
Reading the bibliography…
Generative moment matching network (GMMN) is a deep generative model that differs from Generative Adversarial Network (GAN) by replacing the discriminator in GAN with a two-sample test based on kernel maximum mean discrepancy (MMD).
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Support vector learning for ordinal regression
Ralf Herbrich, Thore Graepel, and Klaus Obermayer · 1999
Earlier work this paper cites.
Deep boltzmann machines
Ruslan Salakhutdinov and Geoffrey Hinton · 2009
Earlier work this paper cites.
Kernel choice and classifiability for rkhs embeddings of probability distributions
Kenji Fukumizu, Arthur Gretton, Gert R Lanckriet, Bernhard Schölkopf, and Bharath K Sriperumbudur · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
Earlier work this paper cites.
Hilbert space embeddings and metrics on probability measures
Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Bernhard Schölkopf, and Gert R.G. Lanckriet · 2010
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.
Optimal kernel choice for large-scale two-sample tests
A. Gretton, B. Sriperumbudur, D. Sejdinovic, H. Strathmann, S. Balakrishnan, M. Pontil, and K. Fukumizu · 2012
Earlier work this paper cites.
Optimal kernel choice for large-scale two-sample tests
Arthur Gretton, Dino Sejdinovic, Heiko Strathmann, Sivaraman Balakrishnan, Massimiliano Pontil, Kenji Fukumizu, and Bharath K Sriperumbudur · 2012
Earlier work this paper cites.
Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
Tijmen Tieleman and Geoffrey Hinton · 2012
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
All of statistics: a concise course in statistical inference
Larry Wasserman · 2013
Earlier work this paper cites.
Generative adversarial nets
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Show, attend and tell: Neural image caption generation with visual attention
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio · 2015
Cited alongside, same era.
Generative moment matching networks
Yujia Li, Kevin Swersky, and Richard Zemel · 2015
Cited alongside, same era.
Training generative neural networks via maximum mean discrepancy optimization
Gintare Karolina Dziugaite, Daniel M. Roy, and Zoubin Ghahramani · 2015
Cited alongside, same era.
Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao · 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.
Wasserstein GAN
Martín Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Closest in time.
Generative models and model criticism via optimized maximum mean discrepancy
Dougal J. Sutherland, Hsiao-Yu Fish Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alexander J. Smola, and Arthur Gretton · 2017
Closest in time.
Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville · 2017
Closest in time.
Generalization and equilibrium in generative adversarial nets (gans)
Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, and Yi Zhang · 2017
Closest in time.
Mcgan: Mean and covariance feature matching gan
Youssef Mroueh, Tom Sercu, and Vaibhava Goel · 2017
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, and Brendan Frey · 2015
Cited alongside, same era.
f-gan: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
Cited alongside, same era.
Kernel mean embedding of distributions: A review and beyonds
Krikamol Muandet, Kenji Fukumizu, Bharath Sriperumbudur, and Bernhard Schölkopf · 2016
Cited alongside, same era.
Deep kernel learning
Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, and Eric P Xing · 2016
Cited alongside, same era.
Generative adversarial networks as variational training of energy based models
Shuangfei Zhai, Yu Cheng, Rogério Schmidt Feris, and Zhongfei Zhang · 2016
Cited alongside, same era.
Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2016
Cited alongside, same era.
Werner Zellinger, Thomas Grubinger, Edwin Lughofer, Thomas Natschläger, and Susanne Saminger-Platz · 2017
Closest in time.
Adversarial generator-encoder networks
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2017
Closest in time.
Began: Boundary equilibrium generative adversarial networks
David Berthelot, Tom Schumm, and Luke Metz · 2017
Closest in time.
The cramer distance as a solution to biased wasserstein gradients
Marc G Bellemare, Ivo Danihelka, Will Dabney, Shakir Mohamed, Balaji Lakshminarayanan, Stephan Hoyer, and Rémi Munos · 2017
Closest in time.
Notes on the cramer gan
Arthur Gretton · 2017
Closest in time.
Improving generative adversarial networks with denoising feature matching
D Warde-Farley and Y Bengio · 2017
Closest in time.
Adversarially learned inference
Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Alex Lamb, Martin Arjovsky, Olivier Mastropietro, and Aaron Courville · 2017
Closest in time.