Fetching the paper…
Reading the bibliography…
This paper addresses the mode collapse for generative adversarial networks (GANs).
Extensions of lipschitz mappings into a hilbert space
William B Johnson and Joram Lindenstrauss · 1984
Earlier work this paper cites.
On lipschitz embedding of finite metric spaces in hilbert space
Jean Bourgain · 1985
Earlier work this paper cites.
An approximate max-flow min-cut theorem for uniform multicommodity flow problems with applications to approximation algorithms
Tom Leighton and Satish Rao · 1988
Earlier work this paper cites.
The three sigma rule
Friedrich Pukelsheim · 1994
Earlier work this paper cites.
The geometry of graphs and some of its algorithmic applications
Nathan Linial, Eran London, and Yuri Rabinovich · 1995
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Embedding finite metric spaces into normed spaces
Jiří Matoušek · 2002
Earlier work this paper cites.
Low-distortion embeddings of finite metric spaces
Piotr Indyk and Jirı Matoušek · 2004
Earlier work this paper cites.
Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
Earlier work this paper cites.
Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
Earlier work this paper cites.
Autoencoding beyond pixels using a learned similarity metric
Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, and Ole Winther · 2015
Earlier work this paper cites.
A note on the evaluation of generative models
Lucas Theis, Aäron van den Oord, and Matthias Bethge · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 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.
Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Cited alongside, same era.
Adversarially learned inference
Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Olivier Mastropietro, Alex Lamb, Martin Arjovsky, and Aaron Courville · 2016
Cited alongside, same era.
Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell · 2016
Cited alongside, same era.
Compressed sensing using generative models
Ashish Bora, Ajil Jalal, Eric Price, and Alexandros G Dimakis · 2017
Later among the works it cites.
Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2017
Later among the works it cites.
Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
Later among the works it cites.
Least squares generative adversarial networks
Xudong Mao, Qing Li, Haoran Xie, Raymond YK Lau, Zhen Wang, and Stephen Paul Smolley · 2017
Later among the works it cites.
Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Generative adversarial text to image synthesis
Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, and Honglak Lee · 2016
Cited alongside, same era.
Unrolled generative adversarial networks
Luke Metz, Ben Poole, David Pfau, and Jascha Sohl-Dickstein · 2016
Cited alongside, same era.
Mode regularized generative adversarial networks
Tong Che, Yanran Li, Athul Paul Jacob, Yoshua Bengio, and Wenjie Li · 2016
Cited alongside, same era.
Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
Cited alongside, same era.
Photo-realistic single image super-resolution using a generative adversarial network
Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al · 2016
Cited alongside, same era.
Generative visual manipulation on the natural image manifold
Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman, and Alexei A Efros · 2016
Cited alongside, same era.
Generating videos with scene dynamics
Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba · 2016
Cited alongside, same era.
Bayesian gan
Yunus Saatci and Andrew G Wilson · 2017
Later among the works it cites.
Generalization and equilibrium in generative adversarial nets (gans)
Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, and Yi Zhang · 2017
Later among the works it cites.
Progressive growing of gans for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2017
Later among the works it cites.
Optimizing the latent space of generative networks
Piotr Bojanowski, Armand Joulin, David Lopez-Paz, and Arthur Szlam · 2017
Later among the works it cites.
Learning wasserstein embeddings
Nicolas Courty, Rémi Flamary, and Mélanie Ducoffe · 2017
Later among the works it cites.
Gradient descent gan optimization is locally stable
Vaishnavh Nagarajan and J Zico Kolter · 2017
Later among the works it cites.
Mmd gan: Towards deeper understanding of moment matching network
Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, and Barnabás Póczos · 2017
Later among the works it cites.
Adagan: Boosting generative models
Ilya O Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, and Bernhard Schölkopf · 2017
Later among the works it cites.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Later among the works it cites.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Later among the works it cites.
Ambientgan: Generative models from lossy measurements
Ashish Bora, Eric Price, and Alexandros G Dimakis · 2018
Closest in time.
Pros and cons of gan evaluation measures
Ali Borji · 2018
Closest in time.