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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: 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: 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: 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: 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: Yi Li Publisher: Springer Nature ISBN: 9811391483 Category : Computers Languages : en Pages : 104
Book Description
This book presents a comprehensive review of heterogeneous face analysis and synthesis, ranging from the theoretical and technical foundations to various hot and emerging applications, such as cosmetic transfer, cross-spectral hallucination and face rotation. Deep generative models have been at the forefront of research on artificial intelligence in recent years and have enhanced many heterogeneous face analysis tasks. Not only has there been a constantly growing flow of related research papers, but there have also been substantial advances in real-world applications. Bringing these together, this book describes both the fundamentals and applications of heterogeneous face analysis and synthesis. Moreover, it discusses the strengths and weaknesses of related methods and outlines future trends. Offering a rich blend of theory and practice, the book represents a valuable resource for students, researchers and practitioners who need to construct face analysis systems with deep generative networks.
Author: Shuo Yang Publisher: Springer Nature ISBN: 981197943X Category : Computers Languages : en Pages : 390
Book Description
This two-volume set (CCIS 1700-1701) constitutes the refereed proceedings from the 7th International Symposium on Artificial Intelligence, ISAIR 2022, held in Shanghai, China, in October 2022. The 67 presented papers were thoroughly reviewed and selected from 285 submissions. The volumes present the state-of-the-art contributions on the cognitive intelligence, computer vision, multimedia, Internet of Things, robotics, and related applications.
Author: Vittorio Ferrari Publisher: Springer ISBN: 303001231X Category : Computers Languages : en Pages : 757
Book Description
The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.
Author: Hiroshi Ishikawa Publisher: Springer Nature ISBN: 3030695387 Category : Computers Languages : en Pages : 730
Book Description
The six volume set of LNCS 12622-12627 constitutes the proceedings of the 15th Asian Conference on Computer Vision, ACCV 2020, held in Kyoto, Japan, in November/ December 2020.* The total of 254 contributions was carefully reviewed and selected from 768 submissions during two rounds of reviewing and improvement. The papers focus on the following topics: Part I: 3D computer vision; segmentation and grouping Part II: low-level vision, image processing; motion and tracking Part III: recognition and detection; optimization, statistical methods, and learning; robot vision Part IV: deep learning for computer vision, generative models for computer vision Part V: face, pose, action, and gesture; video analysis and event recognition; biomedical image analysis Part VI: applications of computer vision; vision for X; datasets and performance analysis *The conference was held virtually.
Author: Qiang Zhu
Author: Qiang Zhu
Author: Roshani Raut
Author: 
Author: Arun Solanki
Author: Milan Sonka
Author: Yi Li
Author: Shuo Yang
Author: Jason Brownlee
Author: Vittorio Ferrari
Author: Hiroshi Ishikawa