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Author: Anshu Wang Publisher: ISBN: Category : Languages : en Pages : 44
Book Description
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.
Author: Anshu Wang Publisher: ISBN: Category : Languages : en Pages : 44
Book Description
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.
Author: Arun Solanki Publisher: Academic Press ISBN: 0128236132 Category : Science Languages : en Pages : 444
Book Description
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: Roshani Raut Publisher: CRC Press ISBN: 1000840565 Category : Computers Languages : en Pages : 286
Book Description
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: Xudong Mao Publisher: Springer Nature ISBN: 9813360488 Category : Computers Languages : en Pages : 77
Book Description
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: Soon Yau Cheong Publisher: Packt Publishing Ltd ISBN: 1838821104 Category : Computers Languages : en Pages : 306
Book Description
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.
Author: Publisher: ISBN: Category : Image processing Languages : en Pages : 0
Book Description
Neural networks are a powerful machine learning tool, especially when trained on a large dataset of relevant high-quality data. Generative adversarial networks, image super resolution and most other image manipulation neural networks require a dataset of images and matching target images for training. Collecting and compiling that data can be time consuming and expensive. This work explores an approach for building a dataset of paired document images with a matching scanned version of each document without physical printers or scanners. A dataset of these document image pairs could be used to train a generative adversarial network or image super resolution neural network to convert a scanned document into a pristine document free of artifacts. It could also be used in optical character recognition of scanned documents to improve understanding of documents with degraded quality. Generating a dataset like this without mechanical hardware saves time and materials and has the potential to build similar paired image datasets for other applications. The proposed approach centers on conditional generative adversarial networks to generate the paired dataset from unpaired document images. This work explores StyleGAN2, CycleGAN, CUT, Pix2PixHD, SPADE and SEAN. I find that the base version of each model is currently insufficient for this task.
Author: Kailash Ahirwar Publisher: Packt Publishing Ltd ISBN: 1789134196 Category : Mathematics Languages : en Pages : 310
Book Description
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: Qiang Zhu Publisher: ISBN: Category : Languages : en Pages : 41
Book Description
Style transfer plays a vital role in image manipulation and creates new artistic works in different artistic styles from existing photographs. While style transfer has been widely studied, recovering photo-realistic images from corresponding artistic works has not been fully investigated. And all previous work considers style transfer and removal as separate problems. In this thesis, we present a method to transfer the style of a stylized face to a different face without style and recover photo-realistic face from the same stylized face image simultaneously. Here, style refers to the local patterns or textures of the stylized images. Style transfer gives a new way for artistic creation while style removal can be beneficial for face verification, photo-realistic content editing or facial analysis. Our approach contains two components: the Style Transfer Network (STN) and the Style Removal Network (SRN). STN renders the style of the stylized image to the non-stylized image, and the SRN is designed to remove the style of a stylized photo. By applying the two networks successively to an original input photo, the output should match the input photo. The experiment results in a variety of portraits and styles demonstrate our approach's effectiveness.
Author: David Foster Publisher: "O'Reilly Media, Inc." ISBN: 1492041890 Category : Computers Languages : en Pages : 360
Book Description
Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative. Discover how variational autoencoders can change facial expressions in photos Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation Create recurrent generative models for text generation and learn how to improve the models using attention Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN
Author: Anshu Wang
Author: Anshu Wang
Author: Arun Solanki
Author: Roshani Raut
Author: Xudong Mao
Author: Soon Yau Cheong
Author: 
Author: Jason Brownlee
Author: Kailash Ahirwar
Author: Qiang Zhu
Author: David Foster