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maryam Amiri

maryam Amiri

Academic rank: Assistant Professor
ORCID: https://orcid.org/0000-0002-7411-9552
Education: PhD.
ScopusId: 57146848900
Faculty: Engineering
Address:
Phone: 32625522

Research

Title
Generative Adversarial Networks: zero-sum game in game theory
Type
Presentation
Keywords
Generative Adversarial Network (GAN), Game Theory, Zero-sum Game, Nash Equilibrium, Deep Learning
Year
2022
Researchers maryam Amiri ، Uranus Kazemi

Abstract

Generative Adversarial Networks (GAN) has recently received considerable attention in the intelligence community because of their ability to generate high quality and significant data. GAN is a game between two players where one player’s loss is the gain of another and that is a way to reach Nash that is balanced by the sum of zero. Despite these networks over the years, this paper examines the theoretical aspects of the game in GAN and how it plays. Then the research discusses the type of game in these networks. Later, after examining the challenges of this network, it will be implemented while maintaining equilibrium.