Description

Book Synopsis

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.




Table of Contents

An Introduction to Generative Adversarial Learning: Architectures and Applications.- Generative Adversarial Networks: A Survey on Training, Variants, and Applications.- Fair Data Generation and Machine Learning through Generative Adversarial Networks.

Generative Adversarial Learning: Architectures and Applications

    Product form

    A Hardback by Roozbeh Razavi-Far, Ariel Ruiz-Garcia, Vasile Palade

    £134.99

    Includes FREE delivery

    RRP £149.99 – you save £15.00 (10%)

    Order before 4pm on Monday for delivery by Wed 18 Nov 2026.

    1 in stock

      Trusted by thousands of customers. See 2,385+ Customer Reviews

      View other formats and editions of Generative Adversarial Learning: Architectures and Applications by Roozbeh Razavi-Far

      Book details

      Publisher Springer Nature
      Published 8 February 2022
      ISBN-13 9783030913892
      978-3030913892
      ISBN-10 3030913899
      Also in:
      Artificial intelligence (AI)

      Description

      Book Synopsis

      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.




      Table of Contents

      An Introduction to Generative Adversarial Learning: Architectures and Applications.- Generative Adversarial Networks: A Survey on Training, Variants, and Applications.- Fair Data Generation and Machine Learning through Generative Adversarial Networks.

      Recently viewed products

      © 2026 Book Curl

        • American Express
        • Apple Pay
        • Diners Club
        • Discover
        • Google Pay
        • Maestro
        • Mastercard
        • PayPal
        • Shop Pay
        • Union Pay
        • Visa

        Login

        Forgot your password?

        Don't have an account yet?
        Create account