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Description

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.
Release date Australia
February 8th, 2022
Contributors
  • Edited by Ariel Ruiz-Garcia
  • Edited by Juergen Schmidhuber
  • Edited by Roozbeh Razavi-Far
  • Edited by Vasile Palade
Pages
355
Edition
1st ed. 2022
Audience
  • Professional & Vocational
Illustrations
132 Illustrations, color; 13 Illustrations, black and white; XIV, 355 p. 145 illus., 132 illus. in color.
ISBN-13
9783030913892
Product ID
35663895

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