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Generative adversarial learningarchitectures and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Generative adversarial learningedited by Roozbeh Razavi-Far ... [et al.].
Reminder of title:	architectures and applications /
other author:	Razavi-Far, Roozbeh.
Published:	Cham :Springer International Publishing :2022.
Description:	xiv, 355 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-3-030-91390-8
ISBN:	9783030913908$q(electronic bk.)

Generative adversarial learningarchitectures and applications /
Generative adversarial learningarchitectures and applications /[electronic resource] :edited by Roozbeh Razavi-Far ... [et al.]. - Cham :Springer International Publishing :2022. - xiv, 355 p. :ill., digital ;24 cm. - Intelligent systems reference library,v. 2171868-4408 ;. - Intelligent systems reference library ;v.24..

An Introduction to Generative Adversarial Learning: Architectures and Applications -- Generative Adversarial Networks: A Survey on Training, Variants, and Applications -- Fair Data Generation and Machine Learning through Generative Adversarial Networks.

This book provides a collection of recent research works addressing theoretical issues on improving the learning process and the generalization of GANs as well as state-of-the-art applications of GANs to various domains of real life. Adversarial learning fascinates the attention of machine learning communities across the world in recent years. Generative adversarial networks (GANs), as the main method of adversarial learning, achieve great success and popularity by exploiting a minimax learning concept, in which two networks compete with each other during the learning process. Their key capability is to generate new data and replicate available data distributions, which are needed in many practical applications, particularly in computer vision and signal processing. The book is intended for academics, practitioners, and research students in artificial intelligence looking to stay up to date with the latest advancements on GANs' theoretical developments and their applications.

ISBN: 9783030913908$q(electronic bk.)

Standard No.: 10.1007/978-3-030-91390-8doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .G45 2022

Dewey Class. No.: 006.31

Generative adversarial learningarchitectures and applications /
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490 1 $a Intelligent systems reference library, $x 1868-4408 ; $v v. 217
505 0 $a An Introduction to Generative Adversarial Learning: Architectures and Applications -- Generative Adversarial Networks: A Survey on Training, Variants, and Applications -- Fair Data Generation and Machine Learning through Generative Adversarial Networks.
520 $a This book provides a collection of recent research works addressing theoretical issues on improving the learning process and the generalization of GANs as well as state-of-the-art applications of GANs to various domains of real life. Adversarial learning fascinates the attention of machine learning communities across the world in recent years. Generative adversarial networks (GANs), as the main method of adversarial learning, achieve great success and popularity by exploiting a minimax learning concept, in which two networks compete with each other during the learning process. Their key capability is to generate new data and replicate available data distributions, which are needed in many practical applications, particularly in computer vision and signal processing. The book is intended for academics, practitioners, and research students in artificial intelligence looking to stay up to date with the latest advancements on GANs' theoretical developments and their applications.
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