Generative Adversarial Networks: zero-sum game in game theory
عنوان مقاله: Generative Adversarial Networks: zero-sum game in game theory
شناسه ملی مقاله: DCBDP07_048
منتشر شده در هفتمین کنفرانس ملی و اولین کنفرانس بین المللی محاسبات توزیعی و پردازش داده های بزرگ در سال 1401
شناسه ملی مقاله: DCBDP07_048
منتشر شده در هفتمین کنفرانس ملی و اولین کنفرانس بین المللی محاسبات توزیعی و پردازش داده های بزرگ در سال 1401
مشخصات نویسندگان مقاله:
Uranus Kazemi - Department of Computer Engineering Faculty of Engineering, Arak University ۳۸۱۵۶-۸-۸۳۴۹ Arak, Iran
Maryam Amiri - Department of Computer Engineering Faculty of Engineering, Arak University ۳۸۱۵۶-۸-۸۳۴۹ Arak, Iran
خلاصه مقاله:
Uranus Kazemi - Department of Computer Engineering Faculty of Engineering, Arak University ۳۸۱۵۶-۸-۸۳۴۹ Arak, Iran
Maryam Amiri - Department of Computer Engineering Faculty of Engineering, Arak University ۳۸۱۵۶-۸-۸۳۴۹ Arak, Iran
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.
کلمات کلیدی: Generative Adversarial Network (GAN), Game Theory, Zero-sum Game, Nash Equilibrium, Deep Learning
صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/1453930/