1st Edition

Generative Adversarial Networks and Deep Learning Theory and Applications

    222 Pages 106 B/W Illustrations
    by Chapman & Hall

    222 Pages 106 B/W Illustrations
    by Chapman & Hall

    This book explores how to use generative adversarial networks in a variety of applications and emphasises their substantial advancements over traditional generative models. This book's major goal is to concentrate on cutting-edge research in deep learning and generative adversarial networks, which includes creating new tools and methods for processing text, images, and audio.

    A Generative Adversarial Network (GAN) is a class of machine learning framework and is the next emerging network in deep learning applications. Generative Adversarial Networks(GANs) have the feasibility to build improved models, as they can generate the sample data as per application requirements. There are various applications of GAN in science and technology, including computer vision, security, multimedia and advertisements, image generation, image translation,text-to-images synthesis, video synthesis, generating high-resolution images, drug discovery, etc.


    • Presents a comprehensive guide on how to use GAN for images and videos.
    • Includes case studies of Underwater Image Enhancement Using Generative Adversarial Network, Intrusion detection using GAN
    • Highlights the inclusion of gaming effects using deep learning methods
    • Examines the significant technological advancements in GAN and its real-world application.
    • Discusses as GAN challenges and optimal solutions

    The book addresses scientific aspects for a wider audience such as junior and senior engineering, undergraduate and postgraduate students, researchers, and anyone interested in the trends development and opportunities in GAN and Deep Learning.

    The material in the book can serve as a reference in libraries, accreditation agencies, government agencies, and especially the academic institution of higher education intending to launch or reform their engineering curriculum

    Chapter 1. Generative Adversarial Networks and Its Use cases

    Chaitrali Sorde, Anuja Jadhav, Swati Jaiswal, Hirkani Padwad, Roshani Raut

    Chapter 2. Image-to-Image Translation using Generative Adversarial Networks

    Digvijay Desai, Shreyash Zanjal, Abhishek Kasar, Jayashri Bagade, Yogesh Dandawate

    Chapter 3. Image Editing Using Generative Adversarial Network

    Anuja Jadhav, Chaitrali landge, Swati Jaiswal, Roshani Raut, Atul Kathole,

    Chapter 4. Generative Adversarial Networks for Video-to-Video Translation 

    Yogini Borole, Roshani Raut

    Chapter 5. Security Issues in Generative Adversarial Networks

    Atul B. Kathole, Kapil N. Vhatkar, Roshani Raut, Sonali D. Patil, Anuja Jadhav,

    Chapter 6. Generative Adversarial Networks-aided Intrusion Detection System

    V. Kumar

    Chapter 7. Textual Description to Facial Image Generation

    Vatsal Khandor, Naitik Rathod, Yash Goda, Nemil Shah, Ramchandra Mangrulkar

    Chapter 8. An Application of Generative Adversarial Network in Natural Language Generation

    Pradnya Borkar, Reena Thakur, Parul Bhanarkar

    Chapter 9. Beyond Image Synthesis: GAN and Audio

    Yogini Borole, Roshani Raut

    Chapter 10. A Study on the Application Domains of Electroencephalogram for the Deep Learning-Based Transformative Healthcare

    Suchitra Paul, Ahona Ghosh

    Chapter 11. Emotion Detection using Generative Adversarial Network

    Sima Das, Ahona Ghosh

    Chapter 12. Underwater Image Enhancement Using Generative Adversarial Network

    Nisha Singh Gaur, Mukesh D. Patil, Gajanan K. Birajdar

    Chapter 13. Towards GAN Challenges and its Optimal Solutions

    Harmeet Kaur Khanuja, Aarti Amod Agarkar



    Roshani Raut, Sonali Patil