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Generative adversarial networks and deep learning : theory and applications / edited by Roshani Raut, Pranav D Pathak, Sachin R Sakhare, Sonali Patil.

Contributor(s): Material type: TextPublisher: Boca Raton, FL : CRC Press, 2023Copyright date: ©2023Edition: First editionDescription: 1 online resource (xiv, 208 pages) : illustrationsContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781003203964
  • 1003203965
  • 9781000840551
  • 1000840557
  • 9781000840568
  • 1000840565
Subject(s): DDC classification:
  • 006.3/1 23/eng20221229
LOC classification:
  • Q325.5 .G44 2023
Online resources: Summary: "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"-- Provided by publisher.
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"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"-- Provided by publisher.

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