Understanding GANs: the LQG Setting

Understanding GANs: the LQG Setting

30 October 2017

Papers citing "Understanding GANs: the LQG Setting"

11 / 11 papers shown

Title
Understanding Overparameterization in Generative Adversarial Networks Yogesh Balaji M. Sajedi N. Kalibhat Mucong Ding Dominik Stöger Mahdi Soltanolkotabi S. Feizi AI4CE 12 21 0 12 Apr 2021
Rates of convergence for density estimation with generative adversarial networks Nikita Puchkin S. Samsonov Denis Belomestny Eric Moulines A. Naumov 24 10 0 30 Jan 2021
EigenGame: PCA as a Nash Equilibrium I. Gemp Brian McWilliams Claire Vernade T. Graepel 11 46 0 01 Oct 2020
GANs May Have No Nash Equilibria Farzan Farnia Asuman Ozdaglar GAN 17 43 0 21 Feb 2020
A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications Jie Gui Zhenan Sun Yonggang Wen Dacheng Tao Jieping Ye EGVM 26 817 0 20 Jan 2020
Differentiable Game Mechanics Alistair Letcher David Balduzzi S. Racanière James Martens Jakob N. Foerster K. Tuyls T. Graepel 29 79 0 13 May 2019
2-Wasserstein Approximation via Restricted Convex Potentials with Application to Improved Training for GANs Amirhossein Taghvaei Amin Jalali 17 42 0 19 Feb 2019
Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs Yogesh Balaji Hamed Hassani Rama Chellappa S. Feizi GAN DRL 38 20 0 09 Oct 2018
Global Convergence to the Equilibrium of GANs using Variational Inequalities I. Gemp Sridhar Mahadevan 12 50 0 04 Aug 2018
Computation of optimal transport and related hedging problems via penalization and neural networks Stephan Eckstein Michael Kupper OT 23 49 0 23 Feb 2018
Limit laws of the empirical Wasserstein distance: Gaussian distributions Thomas Rippl Axel Munk A. Sturm 36 64 0 15 Jul 2015