Book Image

Generative Adversarial Networks Projects

By : Kailash Ahirwar
Book Image

Generative Adversarial Networks Projects

By: Kailash Ahirwar

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)

Practical applications of CycleGANs

There are many applications of CycleGANs. In this chapter, we have used a CycleGAN to turn paintings into photos. They can also be used in the following cases:

  • Style transfer: For example, turning photos into paintings and vice versa, turning pictures of horses into zebras and vice versa, and turning pictures of oranges into pictures of apples and vice versa
  • Photo enhancement: CycleGANs can be used to enhance the quality of pictures
  • Season transfer: For example, turning a picture of winter into a picture of summer and vice versa
  • Game style transfer: CycleGANs can be used to transfer the style of Game A to Game B