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  • © 2022

Generative Adversarial Learning: Architectures and Applications

Editors:

(view affiliations)
  • 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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USD 149.00
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  • ISBN: 978-3-030-91390-8
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Table of contents (14 chapters)

  1. Front Matter

    Pages i-xiv
  2. An Introduction to Generative Adversarial Learning: Architectures and Applications

    • Roozbeh Razavi-Far, Ariel Ruiz-Garcia, Vasile Palade
    Pages 1-6
  3. Generative Adversarial Networks: A Survey on Training, Variants, and Applications

    • Maryam Farajzadeh-Zanjani, Roozbeh Razavi-Far, Mehrdad Saif, Vasile Palade
    Pages 7-29
  4. Fair Data Generation and Machine Learning Through Generative Adversarial Networks

    • Xintao Wu, Depeng Xu, Shuhan Yuan, Lu Zhang
    Pages 31-55
  5. Quaternion Generative Adversarial Networks

    • Eleonora Grassucci, Edoardo Cicero, Danilo Comminiello
    Pages 57-86
  6. Image Generation Using Continuous Conditional Generative Adversarial Networks

    • Xin Ding, Yongwei Wang, Zuheng Xu, William J. Welch, Z. Jane Wang
    Pages 87-113
  7. Generative Adversarial Networks for Data Augmentation in Hyperspectral Image Classification

    • Dimitra Koumoutsou, Georgios Siolas, Eleni Charou, Georgios Stamou
    Pages 115-144
  8. Face Aging Using Generative Adversarial Networks

    • Bruno Kemmer, Rodolfo Simões, Clodoaldo Lima
    Pages 145-168
  9. Adversarial Learning in Accelerometer Based Transportation and Locomotion Mode Recognition

    • Lukas Günthermann, Lin Wang, Ivor Simpson, Andrew Philippides, Daniel Roggen
    Pages 205-232
  10. GANs for Molecule Generation in Drug Design and Discovery

    • Ziqiao Zhang, Fei Li, Jihong Guan, Zhenzhou Kong, Liming Shi, Shuigeng Zhou
    Pages 233-273

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.


Keywords

  • Generative Adversarial Networks
  • Deep Learning
  • Artificial Intelligence
  • Neural Networks
  • Machine Learning
  • Data Augmentation
  • Data Synthesis

Editors and Affiliations

  • Department of Electrical and Computer Engineering and School of Computer Science, University of Windsor, Windsor, Canada

    Roozbeh Razavi-Far

  • SeeChange.ai, Manchester, UK

    Ariel Ruiz-Garcia

  • Centre for Computational Science and Mathematical Modelling, Coventry University, Coventry, Switzerland

    Vasile Palade

  • The Swiss AI Lab, IDSIA, University of Lugano, USI & SUPSI, Lugano, Switzerland

    Juergen Schmidhuber

Bibliographic Information

Buying options

eBook
USD 149.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-91390-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book
USD 199.99
Price excludes VAT (USA)