Book Image

Generative Adversarial Networks Cookbook

By : Josh Kalin
Book Image

Generative Adversarial Networks Cookbook

By: Josh Kalin

Overview of this book

Developing Generative Adversarial Networks (GANs) is a complex task, and it is often hard to find code that is easy to understand. This book leads you through eight different examples of modern GAN implementations, including CycleGAN, simGAN, DCGAN, and 2D image to 3D model generation. Each chapter contains useful recipes to build on a common architecture in Python, TensorFlow and Keras to explore increasingly difficult GAN architectures in an easy-to-read format. The book starts by covering the different types of GAN architecture to help you understand how the model works. This book also contains intuitive recipes to help you work with use cases involving DCGAN, Pix2Pix, and so on. To understand these complex applications, you will take different real-world data sets and put them to use. By the end of this book, you will be equipped to deal with the challenges and issues that you may face while working with GAN models, thanks to easy-to-follow code solutions that you can implement right away.
Table of Contents (17 chapters)
Title Page
Copyright and Credits
About Packt

Data augmentation

Data augmentation is the idea that one image can be altered or corrupted to encourage deep learning techniques to generalize for the objective, rather than focusing on single features. In this section, we'll show a simple script for applying different augmentations.

Getting ready

The imgaug library is commonly used in deep learning research and this figure demonstrates a subset of available augmentations in this free-to-use library:

Data augmentation is a cornerstone of deep learning data analysis. Each project needs to understand how data augmentation can improve their project. Why would you choose to include data augmentation in your project? In images, it's easy to understand. By augmenting your data—think flipping, noise, and so on—you are essentially forcing the algorithm to understand the content of the image without memorizing or keying in on singular features. With the advent of deep learners, it's now possible for discriminative modeling techniques to memorize entire...