Overview
- Presents high-quality research articles addressing theoretical work for improving the learning process
- Provides a gentle introduction to GANs and related domains
- Describes most well-known GAN architectures and applications domains
Part of the book series: Intelligent Systems Reference Library (ISRL, volume 217)
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About this book
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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Table of contents (14 chapters)
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Front Matter
Editors and Affiliations
Bibliographic Information
Book Title: Generative Adversarial Learning: Architectures and Applications
Editors: Roozbeh Razavi-Far, Ariel Ruiz-Garcia, Vasile Palade, Juergen Schmidhuber
Series Title: Intelligent Systems Reference Library
DOI: https://doi.org/10.1007/978-3-030-91390-8
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-030-91389-2Published: 08 February 2022
Softcover ISBN: 978-3-030-91392-2Published: 09 February 2023
eBook ISBN: 978-3-030-91390-8Published: 07 February 2022
Series ISSN: 1868-4394
Series E-ISSN: 1868-4408
Edition Number: 1
Number of Pages: XIV, 355
Number of Illustrations: 13 b/w illustrations, 132 illustrations in colour
Topics: Computational Intelligence, Machine Learning, Data Engineering