Generative Adversarial Networks With Industrial Use Cases

Author : Navin K Manaswi
Publisher : BPB Publications
Page : 134 pages
File Size : 42,6 Mb
Release : 2020-03-04
Category : Computers
ISBN : 9789389423853

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Generative Adversarial Networks with Industrial Use Cases by Navin K Manaswi Pdf

Best Book on GAN Ê DESCRIPTIONÊ This book aims at simplifying GAN for everyone. This book is very important for machine learning engineers, researchers, students, professors, and professionals. Universities and online course instructors will find this book very interesting for teaching advanced deep learning, specially Generative Adversarial Networks(GAN). Industry professionals, coders, and data scientists can learn GAN from scratch. They can learn how to build GAN codes for industrial applications for Healthcare, Retail, HRTech, EduTech, Telecom, Media, and Entertainment. Mathematics of GAN is discussed and illustrated. KL divergence and other parts of GAN are illustrated and discussed mathematically. This book teaches how to build codes for pix2pix GAN, DCGAN, CGAN, styleGAN, cycleGAN, and many other GAN. Machine Learning and Deep Learning Researchers will learn GAN in the shortest possible time with the help of this book. Ê KEY FEATURESÊÊ - Understanding the deep learning landscape and GANÕs relevance - Learning basics of GAN - Learning how to build GAN from scratch - Understanding mathematics and limitations of GAN - Understanding GAN applications for Retail, Healthcare, Telecom, Media and EduTech - Understanding the important GAN papers such as pix2pixGAN, styleGAN, cycleGAN, DCGAN - Learning how to build GAN code for industrial applications - Understanding the difference between varieties of GAN WHAT WILL YOU LEARNÊ _ÊMachine Learning Researchers would be comfortable in building advanced deep learning codes for Industrial applications _ÊData Scientists would start solving very complex problems in deep learning _ÊStudents would be ready to join an industry with these skills _ÊAverage data engineers and scientists would be able to develop complex GAN codes to solve the toughest problems in computer vision Ê WHO THIS BOOK IS FORÊÊ This book is perfect for machine learning engineers, data scientists, data engineers, deep learning professionals, and computer vision researchers. This book is also very useful for medical imaging professionals, autonomous vehicles professionals, retail fashion professionals, media & entertainment professionals, edutech and HRtech professionals. Professors and Students working in machine learning, deep learning, computer vision, and industrial applications would find this book extremely useful. TABLE OF CONTENTS 1. Basics of GAN 2. Introduction 3. Problem with GANÊ 4. Famous Types Of GANs

Author : Roshani Raut,Pranav D Pathak,Sachin R Sakhare,Sonali Patil
Publisher : CRC Press
Page : 286 pages
File Size : 40,6 Mb
Release : 2023-04-10
Category : Computers
ISBN : 9781000840568

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Generative Adversarial Networks and Deep Learning by Roshani Raut,Pranav D Pathak,Sachin R Sakhare,Sonali Patil Pdf

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. 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

Author : Kailash Ahirwar
Publisher : Packt Publishing Ltd
Page : 310 pages
File Size : 42,9 Mb
Release : 2019-01-31
Category : Mathematics
ISBN : 9781789134193

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Generative Adversarial Networks Projects by Kailash Ahirwar Pdf

Explore various Generative Adversarial Network architectures using the Python ecosystem Key FeaturesUse different datasets to build advanced projects in the Generative Adversarial Network domainImplement projects ranging from generating 3D shapes to a face aging applicationExplore the power of GANs to contribute in open source research and projectsBook Description Generative Adversarial Networks (GANs) have the potential to build next-generation models, as they can mimic any distribution of data. Major research and development work is being undertaken in this field since it is one of the rapidly growing areas of machine learning. This book will test unsupervised techniques for training neural networks as you build seven end-to-end projects in the GAN domain. Generative Adversarial Network Projects begins by covering the concepts, tools, and libraries that you will use to build efficient projects. You will also use a variety of datasets for the different projects covered in the book. The level of complexity of the operations required increases with every chapter, helping you get to grips with using GANs. You will cover popular approaches such as 3D-GAN, DCGAN, StackGAN, and CycleGAN, and you’ll gain an understanding of the architecture and functioning of generative models through their practical implementation. By the end of this book, you will be ready to build, train, and optimize your own end-to-end GAN models at work or in your own projects. What you will learnTrain a network on the 3D ShapeNet dataset to generate realistic shapesGenerate anime characters using the Keras implementation of DCGANImplement an SRGAN network to generate high-resolution imagesTrain Age-cGAN on Wiki-Cropped images to improve face verificationUse Conditional GANs for image-to-image translationUnderstand the generator and discriminator implementations of StackGAN in KerasWho this book is for If you’re a data scientist, machine learning developer, deep learning practitioner, or AI enthusiast looking for a project guide to test your knowledge and expertise in building real-world GANs models, this book is for you.

Author : Josh Kalin
Publisher : Packt Publishing Ltd
Page : 261 pages
File Size : 49,7 Mb
Release : 2018-12-31
Category : Computers
ISBN : 9781789139587

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Generative Adversarial Networks Cookbook by Josh Kalin Pdf

Simplify next-generation deep learning by implementing powerful generative models using Python, TensorFlow and Keras Key FeaturesUnderstand the common architecture of different types of GANsTrain, optimize, and deploy GAN applications using TensorFlow and KerasBuild generative models with real-world data sets, including 2D and 3D dataBook Description Developing Generative Adversarial Networks (GANs) is a complex task, and it is often hard to find code that is easy to understand. This book leads you through eight different examples of modern GAN implementations, including CycleGAN, simGAN, DCGAN, and 2D image to 3D model generation. Each chapter contains useful recipes to build on a common architecture in Python, TensorFlow and Keras to explore increasingly difficult GAN architectures in an easy-to-read format. The book starts by covering the different types of GAN architecture to help you understand how the model works. This book also contains intuitive recipes to help you work with use cases involving DCGAN, Pix2Pix, and so on. To understand these complex applications, you will take different real-world data sets and put them to use. By the end of this book, you will be equipped to deal with the challenges and issues that you may face while working with GAN models, thanks to easy-to-follow code solutions that you can implement right away. What you will learnStructure a GAN architecture in pseudocodeUnderstand the common architecture for each of the GAN models you will buildImplement different GAN architectures in TensorFlow and KerasUse different datasets to enable neural network functionality in GAN modelsCombine different GAN models and learn how to fine-tune themProduce a model that can take 2D images and produce 3D modelsDevelop a GAN to do style transfer with Pix2PixWho this book is for This book is for data scientists, machine learning developers, and deep learning practitioners looking for a quick reference to tackle challenges and tasks in the GAN domain. Familiarity with machine learning concepts and working knowledge of Python programming language will help you get the most out of the book.

Author : Rafael Valle
Publisher : Packt Publishing Ltd
Page : 263 pages
File Size : 43,5 Mb
Release : 2019-05-03
Category : Mathematics
ISBN : 9781789535136

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Hands-On Generative Adversarial Networks with Keras by Rafael Valle Pdf

Develop generative models for a variety of real-world use-cases and deploy them to production Key FeaturesDiscover various GAN architectures using Python and Keras libraryUnderstand how GAN models function with the help of theoretical and practical examplesApply your learnings to become an active contributor to open source GAN applicationsBook Description Generative Adversarial Networks (GANs) have revolutionized the fields of machine learning and deep learning. This book will be your first step towards understanding GAN architectures and tackling the challenges involved in training them. This book opens with an introduction to deep learning and generative models, and their applications in artificial intelligence (AI). You will then learn how to build, evaluate, and improve your first GAN with the help of easy-to-follow examples. The next few chapters will guide you through training a GAN model to produce and improve high-resolution images. You will also learn how to implement conditional GANs that give you the ability to control characteristics of GAN outputs. You will build on your knowledge further by exploring a new training methodology for progressive growing of GANs. Moving on, you'll gain insights into state-of-the-art models in image synthesis, speech enhancement, and natural language generation using GANs. In addition to this, you'll be able to identify GAN samples with TequilaGAN. By the end of this book, you will be well-versed with the latest advancements in the GAN framework using various examples and datasets, and you will have the skills you need to implement GAN architectures for several tasks and domains, including computer vision, natural language processing (NLP), and audio processing. Foreword by Ting-Chun Wang, Senior Research Scientist, NVIDIA What you will learnLearn how GANs work and the advantages and challenges of working with themControl the output of GANs with the help of conditional GANs, using embedding and space manipulationApply GANs to computer vision, NLP, and audio processingUnderstand how to implement progressive growing of GANsUse GANs for image synthesis and speech enhancementExplore the future of GANs in visual and sonic artsImplement pix2pixHD to turn semantic label maps into photorealistic imagesWho this book is for This book is for machine learning practitioners, deep learning researchers, and AI enthusiasts who are looking for a perfect mix of theory and hands-on content in order to implement GANs using Keras. Working knowledge of Python is expected.

Author : Rafael Valle
Publisher : Unknown
Page : 272 pages
File Size : 45,5 Mb
Release : 2019
Category : Electronic
ISBN : OCLC:1125070873

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Hands-On Generative Adversarial Networks with Keras by Rafael Valle Pdf

Develop generative models for a variety of real-world use cases and deploy them to production Key Features Discover various GAN architectures using a Python and Keras library Understand how GAN models function with the help of theoretical and practical examples Apply your learnings to become an active contributor to open source GAN applications Book Description Generative Adversarial Networks (GANs) have revolutionized the fields of machine learning and deep learning. This book will be your first step toward understanding GAN architectures and tackling the challenges involved in training them. This book opens with an introduction to deep learning and generative models and their applications in artificial intelligence (AI). You will then learn how to build, evaluate, and improve your first GAN with the help of easy-to-follow examples. The next few chapters will guide you through training a GAN model to produce and improve high-resolution images. You will also learn how to implement conditional GANs that enable you to control characteristics of GAN output. You will build on your knowledge further by exploring a new training methodology for progressive growing of GANs. Moving on, you'll gain insights into state-of-the-art models in image synthesis, speech enhancement, and natural language generation using GANs. In addition to this, you'll be able to identify GAN samples with TequilaGAN. By the end of this book, you will be well-versed with the latest advancements in the GAN framework using various examples and datasets, and you will have developed the skills you need to implement GAN architectures for several tasks and domains, including computer vision, natural language processing (NLP), and audio processing. Foreword by Ting-Chun Wang, Senior Research Scientist, NVIDIA What you will learn Discover how GANs work and the advantages and challenges of working with them Control the output of GANs with the help of conditional GANs, using embedding and space manipulation Apply GANs to computer vision, natural language processing (NLP), and audio processing Understand how to implement progressive growing of GANs Use GANs for image synthesis and speech enhancement Explore the future of GANs in visual and sonic arts Implement pix2pixHD to turn semantic label maps into photorealistic images Who this book is for This book is for machine learning practitioners, deep learning researchers, and AI enthusiasts who are looking for a mix of theory and hands-on co ...

Author : Kuntal Ganguly
Publisher : Unknown
Page : 180 pages
File Size : 44,6 Mb
Release : 2017-10-30
Category : Computers
ISBN : 1788396413

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Learning Generative Adversarial Networks by Kuntal Ganguly Pdf

Build image generation and semi-supervised models using Generative Adversarial NetworksAbout This Book* Understand the buzz surrounding Generative Adversarial Networks and how they work, in the simplest manner possible* Develop generative models for a variety of real-world use-cases and deploy them to production* Contains intuitive examples and real-world cases to put the theoretical concepts explained in this book to practical useWho This Book Is ForData scientists and machine learning practitioners who wish to understand the fundamentals of generative models will find this book useful. Those who wish to implement Generative Adversarial Networks and their variant architectures through real-world examples will also benefit from this book. No prior knowledge of generative models or GANs is expected.What You Will Learn* Understand the basics of deep learning and the difference between discriminative and generative models* Generate images and build semi-supervised models using Generative Adversarial Networks (GANs) with real-world datasets* Tune GAN models by addressing the challenges such as mode collapse, training instability using mini batch, feature matching, and the boundary equilibrium technique.* Use stacking with Deep Learning architectures to run and generate images from text.* Couple multiple Generative models to discover relationships across various domains* Explore the real-world steps to deploy deep models in productionIn DetailGenerative models are gaining a lot of popularity among the data scientists, mainly because they facilitate the building of AI systems that consume raw data from a source and automatically builds an understanding of it. Unlike supervised learning methods, generative models do not require labeling of the data which makes it an interesting system to use. This book will help you to build and analyze the deep learning models and apply them to real-world problems. This book will help readers develop intelligent and creative application from a wide variety of datasets, mainly focusing on visuals or images.The book begins with the basics of generative models, as you get to know the theory behind Generative Adversarial Networks and its building blocks. This book will show you how you can overcome the problem of text to image synthesis with GANs, using libraries like Tensorflow, Keras and PyTorch. Transfering style from one domain to another becomes a headache when working with huge data sets. The author, using real-world examples, will show how you can overcome this. You will understand and train Generative Adversarial Networks and use them in a production environment and learn tips to use them effectively and accurately.Style and approachA step-by-step guide that will teach you the use of appropriate GAN models for image generation, editing and painting, text-to-image synthesis, image style transfer, and cross-domain discovery with Python libraries such as Tensorflow, Keras, and PyTorch.

Author : Roozbeh Razavi-Far,Ariel Ruiz-Garcia,Vasile Palade,Juergen Schmidhuber
Publisher : Springer Nature
Page : 355 pages
File Size : 55,9 Mb
Release : 2022-03-11
Category : Technology & Engineering
ISBN : 9783030913908

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Generative Adversarial Learning: Architectures and Applications by Roozbeh Razavi-Far,Ariel Ruiz-Garcia,Vasile Palade,Juergen Schmidhuber Pdf

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.

Author : Zhihan Lyu
Publisher : Springer Nature
Page : 607 pages
File Size : 53,8 Mb
Release : 2024-06-02
Category : Electronic
ISBN : 9783031462382

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Applications of Generative AI by Zhihan Lyu Pdf

Author : Xudong Mao,Qing Li
Publisher : Springer Nature
Page : 77 pages
File Size : 53,7 Mb
Release : 2021-03-21
Category : Computers
ISBN : 9789813360488

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Generative Adversarial Networks for Image Generation by Xudong Mao,Qing Li Pdf

Generative adversarial networks (GANs) were introduced by Ian Goodfellow and his co-authors including Yoshua Bengio in 2014, and were to referred by Yann Lecun (Facebook’s AI research director) as “the most interesting idea in the last 10 years in ML.” GANs’ potential is huge, because they can learn to mimic any distribution of data, which means they can be taught to create worlds similar to our own in any domain: images, music, speech, prose. They are robot artists in a sense, and their output is remarkable – poignant even. In 2018, Christie’s sold a portrait that had been generated by a GAN for $432,000. Although image generation has been challenging, GAN image generation has proved to be very successful and impressive. However, there are two remaining challenges for GAN image generation: the quality of the generated image and the training stability. This book first provides an overview of GANs, and then discusses the task of image generation and the details of GAN image generation. It also investigates a number of approaches to address the two remaining challenges for GAN image generation. Additionally, it explores three promising applications of GANs, including image-to-image translation, unsupervised domain adaptation and GANs for security. This book appeals to students and researchers who are interested in GANs, image generation and general machine learning and computer vision.

Author : Vladimir Bok,Jakub Langr
Publisher : Simon and Schuster
Page : 367 pages
File Size : 53,7 Mb
Release : 2019-09-09
Category : Computers
ISBN : 9781638354239

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GANs in Action by Vladimir Bok,Jakub Langr Pdf

Deep learning systems have gotten really great at identifying patterns in text, images, and video. But applications that create realistic images, natural sentences and paragraphs, or native-quality translations have proven elusive. Generative Adversarial Networks, or GANs, offer a promising solution to these challenges by pairing two competing neural networks' one that generates content and the other that rejects samples that are of poor quality. GANs in Action: Deep learning with Generative Adversarial Networks teaches you how to build and train your own generative adversarial networks. First, you'll get an introduction to generative modelling and how GANs work, along with an overview of their potential uses. Then, you'll start building your own simple adversarial system, as you explore the foundation of GAN architecture: the generator and discriminator networks. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Author : John Hany,Greg Walters
Publisher : Unknown
Page : 312 pages
File Size : 54,5 Mb
Release : 2019-12-12
Category : Computers
ISBN : 1789530512

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Hands-On Generative Adversarial Networks with PyTorch 1.x by John Hany,Greg Walters Pdf

Apply deep learning techniques and neural network methodologies to build, train, and optimize generative network models Key Features Implement GAN architectures to generate images, text, audio, 3D models, and more Understand how GANs work and become an active contributor in the open source community Learn how to generate photo-realistic images based on text descriptions Book Description With continuously evolving research and development, Generative Adversarial Networks (GANs) are the next big thing in the field of deep learning. This book highlights the key improvements in GANs over generative models and guides in making the best out of GANs with the help of hands-on examples. This book starts by taking you through the core concepts necessary to understand how each component of a GAN model works. You'll build your first GAN model to understand how generator and discriminator networks function. As you advance, you'll delve into a range of examples and datasets to build a variety of GAN networks using PyTorch functionalities and services, and become well-versed with architectures, training strategies, and evaluation methods for image generation, translation, and restoration. You'll even learn how to apply GAN models to solve problems in areas such as computer vision, multimedia, 3D models, and natural language processing (NLP). The book covers how to overcome the challenges faced while building generative models from scratch. Finally, you'll also discover how to train your GAN models to generate adversarial examples to attack other CNN and GAN models. By the end of this book, you will have learned how to build, train, and optimize next-generation GAN models and use them to solve a variety of real-world problems. What you will learn Implement PyTorch's latest features to ensure efficient model designing Get to grips with the working mechanisms of GAN models Perform style transfer between unpaired image collections with CycleGAN Build and train 3D-GANs to generate a point cloud of 3D objects Create a range of GAN models to perform various image synthesis operations Use SEGAN to suppress noise and improve the quality of speech audio Who this book is for This GAN book is for machine learning practitioners and deep learning researchers looking to get hands-on guidance in implementing GAN models using PyTorch. You'll become familiar with state-of-the-art GAN architectures with the help of real-world examples. Working knowledge of Python programming language is necessary to grasp the concepts covered in this book.

Author : Arun Solanki,Anand Nayyar,Mohd Naved
Publisher : Elsevier
Page : 444 pages
File Size : 55,8 Mb
Release : 2021-06-23
Category : Science
ISBN : 9780128235195

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Generative Adversarial Networks for Image-to-Image Translation by Arun Solanki,Anand Nayyar,Mohd Naved Pdf

Generative Adversarial Networks (GAN) have started a revolution in Deep Learning, and today GAN is one of the most researched topics in Artificial Intelligence. Generative Adversarial Networks for Image-to-Image Translation provides a comprehensive overview of the GAN (Generative Adversarial Network) concept starting from the original GAN network to various GAN-based systems such as Deep Convolutional GANs (DCGANs), Conditional GANs (cGANs), StackGAN, Wasserstein GANs (WGAN), cyclical GANs, and many more. The book also provides readers with detailed real-world applications and common projects built using the GAN system with respective Python code. A typical GAN system consists of two neural networks, i.e., generator and discriminator. Both of these networks contest with each other, similar to game theory. The generator is responsible for generating quality images that should resemble ground truth, and the discriminator is accountable for identifying whether the generated image is a real image or a fake image generated by the generator. Being one of the unsupervised learning-based architectures, GAN is a preferred method in cases where labeled data is not available. GAN can generate high-quality images, images of human faces developed from several sketches, convert images from one domain to another, enhance images, combine an image with the style of another image, change the appearance of a human face image to show the effects in the progression of aging, generate images from text, and many more applications. GAN is helpful in generating output very close to the output generated by humans in a fraction of second, and it can efficiently produce high-quality music, speech, and images. Introduces the concept of Generative Adversarial Networks (GAN), including the basics of Generative Modelling, Deep Learning, Autoencoders, and advanced topics in GAN Demonstrates GANs for a wide variety of applications, including image generation, Big Data and data analytics, cloud computing, digital transformation, E-Commerce, and Artistic Neural Networks Includes a wide variety of biomedical and scientific applications, including unsupervised learning, natural language processing, pattern recognition, image and video processing, and disease diagnosis Provides a robust set of methods that will help readers to appropriately and judiciously use the suitable GANs for their applications

Author : Cesar Analide,Paulo Novais,David Camacho,Hujun Yin
Publisher : Springer Nature
Page : 633 pages
File Size : 44,8 Mb
Release : 2020-10-29
Category : Computers
ISBN : 9783030623654

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Intelligent Data Engineering and Automated Learning – IDEAL 2020 by Cesar Analide,Paulo Novais,David Camacho,Hujun Yin Pdf

This two-volume set of LNCS 12489 and 12490 constitutes the thoroughly refereed conference proceedings of the 21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020, held in Guimaraes, Portugal, in November 2020.* The 93 papers presented were carefully reviewed and selected from 134 submissions. These papers provided a timely sample of the latest advances in data engineering and machine learning, from methodologies, frameworks, and algorithms to applications. The core themes of IDEAL 2020 include big data challenges, machine learning, data mining, information retrieval and management, bio-/neuro-informatics, bio-inspiredmodels, agents and hybrid intelligent systems, real-world applications of intelligent techniques and AI. * The conference was held virtually due to the COVID-19 pandemic.

Author : Kyoung-Yun Kim,Leslie Monplaisir,Jeremy Rickli
Publisher : Springer Nature
Page : 427 pages
File Size : 46,8 Mb
Release : 2022-10-12
Category : Technology & Engineering
ISBN : 9783031183263

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Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus by Kyoung-Yun Kim,Leslie Monplaisir,Jeremy Rickli Pdf

This is an open access book. It gathers the first volume of the proceedings of the 31st edition of the International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2022, held on June 19 – 23, 2022, in Detroit, Michigan, USA. Covering four thematic areas including Manufacturing Processes, Machine Tools, Manufacturing Systems, and Enabling Technologies, it reports on advanced manufacturing processes, and innovative materials for 3D printing, applications of machine learning, artificial intelligence and mixed reality in various production sectors, as well as important issues in human-robot collaboration, including methods for improving safety. Contributions also cover strategies to improve quality control, supply chain management and training in the manufacturing industry, and methods supporting circular supply chain and sustainable manufacturing. All in all, this book provides academicians, engineers and professionals with extensive information on both scientific and industrial advances in the converging fields of manufacturing, production, and automation.