Generative Adversarial Learning: Architectures and Applications
Language: English
Published by Palgrave Macmillan, 2022
Series: Book 182 of 188 - Intelligent Systems Reference Library
- Hardcover
- New
Condition: New
£ 206.84
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Add to basketItem description from seller
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. Sprache: Englisch Gewicht in Gramm: 671. …
Seller Inventory # IB-1676401
- Title
- Generative Adversarial Learning: Architectures and Applications
- Publisher
- Palgrave Macmillan
- Publication year
- 2022
- Condition
- Neu
- Binding
- Gebundene Ausgabe
- Language
- English
- ISBN 10
- 3030913899
- ISBN 13
- 9783030913892
- Item weight
- 671 grams
- Series
- Book 182 of 188: Intelligent Systems Reference Library
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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.
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