Название: Generative Adversarial Networks and Deep Learning: Theory and Applications
Автор: Roshani Raut, Pranav D Pathak, Sachin R Sakhare, Sonali Patil
Издательство: CRC Press
Год: 2023
Страниц: 223
Язык: английский
Формат: pdf (true)
Размер: 20.7 MB

This book explores how to use Generative Adversarial Network (GANs) 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.

A convolutional neural network or a recurrent neural network can be used as the discriminator network, while a de-convolutional neural network can be used as the generator network. As a result, GANs can be used to build multidimensional data distributions like pictures. GANs have shown potential in a variety of difficult generative tasks, including text-to-photo translation, picture generation, image composition, and image-to-image translation. GANs are a powerful type of deep generative model; however, they have a variety of training issues, such as mode collapse and training instability. There are different types of learning approaches in machine learning such as supervised and unsupervised learning.

Unsupervised Learning is a technique for teaching computers to use data that has not been classified or labeled. It means that no preparation information is accessible and the machine is customized to learn all alone. With no earlier information on the information, the machine should have the option to group it. The objective is to open the machines to huge measures of different information and allow them to gain from it to uncover already obscure bits of knowledge and reveal stowed away examples. Accordingly, solo learning calculations don’t necessarily create unsurprising outcomes. Rather, it figures out what makes the given dataset novel or interesting. It is important to program the machine to learn all alone. Both organized and unstructured information should be perceived and examined by the PC. Solo learning calculations can deal with more complicated handling undertakings than regulated learning frameworks.

Features:

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.