Download or read book Generative Adversarial Networks for Image to Image Translation written by Arun Solanki and published by Academic Press. This book was released on 2021-06-22 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Generative Adversarial Networks and Deep Learning written by Roshani Raut and published by Chapman & Hall/CRC. This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "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"--

Download or read book Generative Adversarial Networks for Image Generation written by Xudong Mao and published by Springer Nature. This book was released on 2021-03-21 with total page 77 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book 2019 3rd International Conference on Electronics Communication and Aerospace Technology ICECA written by IEEE Staff and published by . This book was released on 2019-06-12 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: ICECA 2019 will provide an outstanding international forum for scientists from all over the world to share ideas and achievements in the theory and practice of all areas of aero space technologies Presentations should highlight inventive systems as a concept that combines theoretical research and applications in Electronics, Communication, Information and Aerospace technologies

Download or read book Generative Adversarial Networks with Python written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2019-07-11 with total page 655 pages. Available in PDF, EPUB and Kindle. Book excerpt: Step-by-step tutorials on generative adversarial networks in python for image synthesis and image translation.

Download or read book Image to image Translation Through Generative Adversarial Networks written by Ivana Dukovska and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Practical Convolutional Neural Networks written by Mohit Sewak and published by Packt Publishing Ltd. This book was released on 2018-02-27 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: One stop guide to implementing award-winning, and cutting-edge CNN architectures Key Features Fast-paced guide with use cases and real-world examples to get well versed with CNN techniques Implement CNN models on image classification, transfer learning, Object Detection, Instance Segmentation, GANs and more Implement powerful use-cases like image captioning, reinforcement learning for hard attention, and recurrent attention models Book Description Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative eCommerce and more.You will learn to create innovative solutions around image and video analytics to solve complex machine learning and computer vision related problems and implement real-life CNN models. This book starts with an overview of deep neural networkswith the example of image classification and walks you through building your first CNN for human face detector. We will learn to use concepts like transfer learning with CNN, and Auto-Encoders to build very powerful models, even when not much of supervised training data of labeled images is available. Later we build upon the learning achieved to build advanced vision related algorithms for object detection, instance segmentation, generative adversarial networks, image captioning, attention mechanisms for vision, and recurrent models for vision. By the end of this book, you should be ready to implement advanced, effective and efficient CNN models at your professional project or personal initiatives by working on complex image and video datasets. What you will learn From CNN basic building blocks to advanced concepts understand practical areas they can be applied to Build an image classifier CNN model to understand how different components interact with each other, and then learn how to optimize it Learn different algorithms that can be applied to Object Detection, and Instance Segmentation Learn advanced concepts like attention mechanisms for CNN to improve prediction accuracy Understand transfer learning and implement award-winning CNN architectures like AlexNet, VGG, GoogLeNet, ResNet and more Understand the working of generative adversarial networks and how it can create new, unseen images Who this book is for This book is for data scientists, machine learning and deep learning practitioners, Cognitive and Artificial Intelligence enthusiasts who want to move one step further in building Convolutional Neural Networks. Get hands-on experience with extreme datasets and different CNN architectures to build efficient and smart ConvNet models. Basic knowledge of deep learning concepts and Python programming language is expected.

Download or read book Generative Adversarial Networks with Industrial Use Cases written by Navin K Manaswi and published by BPB Publications. This book was released on 2020-03-04 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Application of Generative Adversarial Network on Image Style Transformation and Image Processing written by Anshu Wang and published by . This book was released on 2018 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: Image-to-Image translation is a collection of computer vision problems that aim to learn a mapping between two different domains or multiple domains. Recent research in computer vision and deep learning produced powerful tools for the task. Conditional adversarial net- works serve as a general-purpose solution for image-to-image translation problems. Deep Convolutional Neural Networks can learn an image representation that can be applied for recognition, detection, and segmentation. Generative Adversarial Networks (GANs) has gained success in image synthesis. However, traditional models that require paired training data might not be applicable in most situations due to lack of paired data. Here we review and compare two different models for learning unsupervised image to im- age translation: CycleGAN and Unsupervised Image-to-Image Translation Networks (UNIT). Both models adopt cycle consistency, which enables us to conduct unsupervised learning without paired data. We show that both models can successfully perform image style trans- lation. The experiments reveal that CycleGAN can generate more realistic results, and UNIT can generate varied images and better preserve the structure of input images.

Download or read book GANs in Action written by Vladimir Bok and published by Simon and Schuster. This book was released on 2019-09-09 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Generative Adversarial Networks Projects written by Kailash Ahirwar and published by Packt Publishing Ltd. This book was released on 2019-01-31 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Hands On Image Generation with TensorFlow written by Soon Yau Cheong and published by Packt Publishing Ltd. This book was released on 2020-12-24 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: Implement various state-of-the-art architectures, such as GANs and autoencoders, for image generation using TensorFlow 2.x from scratch Key FeaturesUnderstand the different architectures for image generation, including autoencoders and GANsBuild models that can edit an image of your face, turn photos into paintings, and generate photorealistic imagesDiscover how you can build deep neural networks with advanced TensorFlow 2.x featuresBook Description The emerging field of Generative Adversarial Networks (GANs) has made it possible to generate indistinguishable images from existing datasets. With this hands-on book, you’ll not only develop image generation skills but also gain a solid understanding of the underlying principles. Starting with an introduction to the fundamentals of image generation using TensorFlow, this book covers Variational Autoencoders (VAEs) and GANs. You’ll discover how to build models for different applications as you get to grips with performing face swaps using deepfakes, neural style transfer, image-to-image translation, turning simple images into photorealistic images, and much more. You’ll also understand how and why to construct state-of-the-art deep neural networks using advanced techniques such as spectral normalization and self-attention layer before working with advanced models for face generation and editing. You'll also be introduced to photo restoration, text-to-image synthesis, video retargeting, and neural rendering. Throughout the book, you’ll learn to implement models from scratch in TensorFlow 2.x, including PixelCNN, VAE, DCGAN, WGAN, pix2pix, CycleGAN, StyleGAN, GauGAN, and BigGAN. By the end of this book, you'll be well versed in TensorFlow and be able to implement image generative technologies confidently. What you will learnTrain on face datasets and use them to explore latent spaces for editing new facesGet to grips with swapping faces with deepfakesPerform style transfer to convert a photo into a paintingBuild and train pix2pix, CycleGAN, and BicycleGAN for image-to-image translationUse iGAN to understand manifold interpolation and GauGAN to turn simple images into photorealistic imagesBecome well versed in attention generative models such as SAGAN and BigGANGenerate high-resolution photos with Progressive GAN and StyleGANWho this book is for The Hands-On Image Generation with TensorFlow book is for deep learning engineers, practitioners, and researchers who have basic knowledge of convolutional neural networks and want to learn various image generation techniques using TensorFlow 2.x. You’ll also find this book useful if you are an image processing professional or computer vision engineer looking to explore state-of-the-art architectures to improve and enhance images and videos. Knowledge of Python and TensorFlow will help you to get the best out of this book.

Download or read book Generative Adversarial Learning Architectures and Applications written by Roozbeh Razavi-Far and published by Springer Nature. This book was released on 2022-03-11 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Hands On Image Generation with TensorFlow written by Soon Yau Cheong and published by . This book was released on 2020-12-24 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Unsupervised Image to image Translation Using Generative Models written by Lei Luo and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years deep learning has achieved great success in various computer vision tasks, such as image classification and segmentation. Unsupervised image-to-image (I2I) translation, which models how to translate images from one domain to another without paired data, lacks systematic and thorough study. In this dissertation I illustrate the significance of studying unsupervised I2I translation, relevant theories, and propose potential approaches to addressing drawbacks and shortcomings in existing works. This dissertation introduces four new contributions in unsupervised I2I translation. The first contribution is the proposal of a unified framework for unsupervised I2I translation. The second contribution is to provide fine-grained control on I2I translation where current approaches fall short. The third contribution of this dissertation is cooperating a module for controlling shapes when translating certain type of images, which require preserving shapes after I2I translation. Lastly, this dissertation proposes a new I2I translation framework that learns to, in an unsupervised manner, only translate objects of interest and leave others unaltered. The first contribution of this work is to address the open problem of multimodal unsupervised I2I translation using a generative adversarial network. Previous works, such as MUNIT and DRIT, are able to translate images among multiple domains, but they generate images of inferior quality and less diverse. Moreover, they require training n(n-1) generators and n discriminators for learning to translate images among n domains. Therefore, I propose a simpler yet more effective framework for multimodal unsupervised I2I translation. The new approach only consists of a mapping network, a encode-decoder pair (generator), and a discriminator. The methods assume that the latent space can be decomposed into content and style sub-spaces by the encoder, where content space is deemed domain-invariant and style space is domain-dependent. Unlike MUNIT and DRIT that simply sample style codes from a standard normal distribution when translating, I employ a mapping network to learn style codes of different domains. Translation is done through the decoder by keeping content codes and exchanging the style codes. To encourage diversity in translated images, I employ style regularizations and inject Guassian noise in the decoder. Extensive experiments show that the new framework is superior to or comparable to state-of-the-art baselines. The second contribution of this dissertation is to add fine-grained control when performing I2I translation. The new framework first assumes that the latent space can be decomposed into content and style sub-spaces. Instead of naively exchanging style codes when translating, the framework uses an interpolator that guides the transformation and produces sequences of intermediate results under different strengths of transformation. Domain specific information, which might still exist in content code and generate inferior images if they are simply treated as domain-invariant, are excluded in our framework. We prove the key assumptions of our framework by establishing some theoretical foundations. Extensive experiments show that the translated images using the new framework are superior or comparable to state-of-the-field baselines. This dissertation also proposes a new I2I translation framework that is shape-aware. Attribute transfer is more challenging when the source and target domain share different shapes, and this new model is able to preserve shape when transferring attributes. Compared to other state-of-art GANs-based image-to-image translation models, the new model is able to generate more visually appealing results while maintaining the quality of results from transfer learning. The last part of this work tries to learn to only translate objects of interest and keep the background unaltered, which produces more visually pleasing results than other approaches. Previous works, such as CycleGAN, MUNIT, and StarGAN2 are able to translate images among multiple domains and generate diverse images, but they often introduce unwanted changes to the background. To improve this, I propose a simple yet effective attention-based framework for unsupervised I2I translation. The framework not only translates solely objects of interests and leave the background unaltered, but also generates images for multiple domains simultaneously. Unlike recent studies on unsupervised I2I with attention mechanism that require ground truth for learning attention maps, the new approach learns attention maps in an unsupervised manner. Extensive experiments show that the new framework is superior to the state-of-the-art baselines.

Download or read book Control of Complex Systems written by Kyriakos Vamvoudakis and published by Butterworth-Heinemann. This book was released on 2016-07-27 with total page 764 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the era of cyber-physical systems, the area of control of complex systems has grown to be one of the hardest in terms of algorithmic design techniques and analytical tools. The 23 chapters, written by international specialists in the field, cover a variety of interests within the broader field of learning, adaptation, optimization and networked control. The editors have grouped these into the following 5 sections: “Introduction and Background on Control Theory”, “Adaptive Control and Neuroscience”, “Adaptive Learning Algorithms”, “Cyber-Physical Systems and Cooperative Control”, “Applications”.The diversity of the research presented gives the reader a unique opportunity to explore a comprehensive overview of a field of great interest to control and system theorists. This book is intended for researchers and control engineers in machine learning, adaptive control, optimization and automatic control systems, including Electrical Engineers, Computer Science Engineers, Mechanical Engineers, Aerospace/Automotive Engineers, and Industrial Engineers. It could be used as a text or reference for advanced courses in complex control systems. • Collection of chapters from several well-known professors and researchers that will showcase their recent work • Presents different state-of-the-art control approaches and theory for complex systems • Gives algorithms that take into consideration the presence of modelling uncertainties, the unavailability of the model, the possibility of cooperative/non-cooperative goals and malicious attacks compromising the security of networked teams • Real system examples and figures throughout, make ideas concrete Includes chapters from several well-known professors and researchers that showcases their recent work Presents different state-of-the-art control approaches and theory for complex systems Explores the presence of modelling uncertainties, the unavailability of the model, the possibility of cooperative/non-cooperative goals, and malicious attacks compromising the security of networked teams Serves as a helpful reference for researchers and control engineers working with machine learning, adaptive control, and automatic control systems

Download or read book Applying Generative Adversarial Networks with Transformer for Image Quality Assessment written by 羅費南 and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: