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)

Summary

In this chapter, we have introduced deep convolutional generative adversarial networks. We started with a basic introduction to DCGANs and then explored the architecture of the DCGAN network in depth. After that, we set up the project and installed the necessary dependencies. Then, we looked at the different steps required to download and prepare the dataset. We then prepared a Keras implementation of the network and trained it on our dataset. Once it was trained, we used it to generate new anime characters. We also explored different applications of DCGAN for real-world use cases.

In the next chapter, we will work on SRGANs for high-resolution image generation.