Fetching the paper…
Reading the bibliography…
We provide theoretical convergence guarantees on training Generative Adversarial Networks (GANs) via SGD.
Distributional and lq̂ norm inequalities for polynomials over convex bodies in rn̂
Anthony Carbery and James Wright · 2001
Earlier work this paper cites.
The theory of max-min and its application to weapons allocation problems
John M Danskin · 2012
Earlier work this paper cites.
Introductory lectures on convex optimization: A basic course
Yurii Nesterov · 2013
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.
Analysis of boolean functions
Ryan O’Donnell · 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.
Mini-batch stochastic approximation methods for nonconvex stochastic composite optimization
Saeed Ghadimi, Guanghui Lan, and Hongchao Zhang · 2016
Earlier work this paper cites.
Unrolled generative adversarial networks
Luke Metz, Ben Poole, David Pfau, and Jascha Sohl-Dickstein · 2016
Earlier work this paper cites.
Towards principled methods for training generative adversarial networks
Martin Arjovsky and Léon Bottou · 2017
Earlier work this paper cites.
Martín Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Earlier work this paper cites.
Generalization and equilibrium in generative adversarial nets (gans)
Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, and Yi Zhang · 2017
Cited alongside, same era.
Understanding gans: the lqg setting
Soheil Feizi, Farzan Farnia, Tony Ginart, and David Tse · 2017
Cited alongside, same era.
Cycada: Cycle-consistent adversarial domain adaptation
Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei A. Efros, and Trevor Darrell · 2017
Cited alongside, same era.
Naveen Kodali, Jacob D. Abernethy, James Hays, and Zsolt Kira · 2017
Cited alongside, same era.
Approximation and convergence properties of generative adversarial learning
Shuang Liu, Olivier Bousquet, and Kamalika Chaudhuri · 2017
Global convergence to the equilibrium of gans using variational inequalities
Ian Gemp and Sridhar Mahadevan · 2018
Later among the works it cites.
Which training methods for gans do actually converge?
Lars Mescheder, Andreas Geiger, and Sebastian Nowozin · 2018
Later among the works it cites.
Non-convex min-max optimization: Provable algorithms and applications in machine learning
Hassan Rafique, Mingrui Liu, Qihang Lin, and Tianbao Yang · 2018
Later among the works it cites.
Photographic text-to-image synthesis with a hierarchically-nested adversarial network
Zizhao Zhang, Yuanpu Xie, and Lin Yang · 2018
Later among the works it cites.
Poincaré recurrence, cycles and spurious equilibria in gradient-descent-ascent for non-convex non-concave zero-sum games, 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Gradient descent gan optimization is locally stable
Vaishnavh Nagarajan and J. Zico Kolter · 2017
Cited alongside, same era.
Efficient statistics, in high dimensions, from truncated samples
Constantinos Daskalakis, Themis Gouleakis, Chistos Tzamos, and Manolis Zampetakis · 2018
Cited alongside, same era.
Efficient statistics, in high dimensions, from truncated samples
Constantinos Daskalakis, Themis Gouleakis, Christos Tzamos, and Manolis Zampetakis · 2018
Cited alongside, same era.
Last-iterate convergence: Zero-sum games and constrained min-max optimization
Constantinos Daskalakis and Ioannis Panageas · 2018
Cited alongside, same era.
The limit points of (optimistic) gradient descent in min-max optimization
Constantinos Daskalakis and Ioannis Panageas · 2018
Cited alongside, same era.
Lampros Flokas, Emmanouil-Vasileios Vlatakis-Gkaragkounis, and Georgios Piliouras · 2019
Later among the works it cites.
Tight analyses for non-smooth stochastic gradient descent
Nicholas JA Harvey, Christopher Liaw, Yaniv Plan, and Sikander Randhawa · 2019
Later among the works it cites.
Simple and optimal high-probability bounds for strongly-convex stochastic gradient descent
Nicholas JA Harvey, Christopher Liaw, and Sikander Randhawa · 2019
Later among the works it cites.
On gradient descent ascent for nonconvex-concave minimax problems
Tianyi Lin, Chi Jin, and Michael I Jordan · 2019
Later among the works it cites.
Sgd learns one-layer networks in wgans, 2019
Qi Lei, Jason D. Lee, Alexandros G. Dimakis, and Constantinos Daskalakis · 2019
Later among the works it cites.
Unsupervised graph representation learning with variable heat kernel
Yongjun Jing, Hao Wang, Kun Shao, Xing Huo, and Yangyang Zhang · 2020
Closest in time.