As we know, convolution layers are really good at processing images. They are capable of learning important features, such as edges, shapes, and complex objects, effectively, as shown in neural networks, such as Inception, AlexNet, Visual Geometry Group (VGG), and ResNet. Ian Goodfellow and others proposed a Generative Adversarial Network (GAN) with dense layers in their paper titled Generative Adversarial Nets, which can be found at the following link: https://arxiv.org/pdf/1406.2661.pdf. Complex neural networks, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) were not initially tested in GANs. The development of Deep Convolutional Generative Adversarial Networks (DCGANs) was an important step toward using CNNs for image generation. A DCGAN uses convolutional layers instead...
Generative Adversarial Networks Projects
By :
Generative Adversarial Networks Projects
By:
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.
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
Other Books You May Enjoy
How would like to rate this book
Customer Reviews
