Generative Adversarial Learning: Architectures and Applications: 217 (Intelligent Systems Reference Library, 217) - Tapa dura

Libro 182 de 188: Intelligent Systems Reference Library
 
9783030913892: Generative Adversarial Learning: Architectures and Applications: 217 (Intelligent Systems Reference Library, 217)
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ISBN 10: 3030913899 ISBN 13: 9783030913892
Editorial: Springer, 2022

Sinopsis

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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De la contraportada

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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Editorial
Springer
Año de publicación
2022
Idioma
Inglés
ISBN 10 
3030913899
ISBN 13 
9783030913892
Encuadernación
Tapa dura
Número de edición
1
Número de páginas
372
Editor
Ruiz-Garcia Ariel, Razavi-Far Roozbeh, Palade Vasile
Contacto del fabricante
no disponible
Persona responsable
CMN Printpool Inh. Mathis Neumann
https://www.also.com/ec/cms5/en_6000/6000/index.jsp

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Hamburg
Alemania

Otras ediciones populares con el mismo título

9783030913922: Generative Adversarial Learning: Architectures and Applications (Intelligent Systems Reference Library)

Edición Destacada

ISBN 10:  3030913929 ISBN 13:  9783030913922
Editorial: Springer, 2023
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