Super-Resolution Generative Adversarial Network, or SRGAN, is a Generative Adversarial Network (GAN) that can generate super-resolution images from low-resolution images, with finer details and higher quality. CNNs were earlier used to produce high-resolution images that train quicker and achieve high-level accuracy. However, in some cases, they are incapable of recovering finer details and often generate blurry images. In this chapter, we will implement an SRGAN network in the Keras framework that will be capable of generating high-resolution images. SRGANs were introduced in the paper titled, Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, by Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, and others, which is available at the following link: https://arxiv.org...
Generative Adversarial Networks Projects
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Generative Adversarial Networks Projects
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Overview of this book
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.
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Generative Adversarial Networks Projects
Table of Contents (11 chapters)
Preface
Free Chapter
Introduction to Generative Adversarial Networks
3D-GAN - Generating Shapes Using GANs
Face Aging Using Conditional GAN
Generating Anime Characters Using DCGANs
Using SRGANs to Generate Photo-Realistic Images
StackGAN - Text to Photo-Realistic Image Synthesis
CycleGAN - Turn Paintings into Photos
Conditional GAN - Image-to-Image Translation Using Conditional Adversarial Networks
Predicting the Future of GANs
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