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 StackGAN

The industry applications of a StackGAN include the following:

  • Generating high-resolution images automatically for entertainment purposes or educational purposes
  • Creating comics: With the use of a StackGAN, the process of creating comics can be reduced to days as the StackGAN can generate comics automatically and assist in the creative process
  • Movie creation: A StackGAN can assist a movie creator by generating frames from text descriptions
  • Art creation: A StackGAN can assist an artist by generating sketches from text descriptions