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A game-theoretic approach for Generative Adversarial Networks
v1v2 (latest)

A game-theoretic approach for Generative Adversarial Networks

IEEE Conference on Decision and Control (CDC), 2020
30 March 2020
Barbara Franci
Sergio Grammatico
    GAN
ArXiv (abs)PDFHTML

Papers citing "A game-theoretic approach for Generative Adversarial Networks"

3 / 3 papers shown
Convergence of sequences: a survey
Convergence of sequences: a surveyAnnual Reviews in Control (ARC), 2021
Barbara Franci
Sergio Grammatico
325
22
0
22 Nov 2021
On the Complexity of Computing Markov Perfect Equilibrium in General-Sum
  Stochastic Games
On the Complexity of Computing Markov Perfect Equilibrium in General-Sum Stochastic Games
Xiaotie Deng
Ningyuan Li
D. Mguni
Jun Wang
Yaodong Yang
319
61
0
04 Sep 2021
Training Generative Adversarial Networks via stochastic Nash games
Training Generative Adversarial Networks via stochastic Nash gamesIEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), 2020
Barbara Franci
Sergio Grammatico
GAN
333
22
0
17 Oct 2020
1
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