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

Balancing data

Balancing data and handling anomalous data are often thought of as the same process. In our case, data balancing involves understanding the techniques used to spread anomalous data without disrupting the underlying data distribution. In this recipe, we will discuss the core concepts in data balancing.

Getting ready

Generative modeling is attempting to build a model that represents the entire data distribution. In order to learn this underlying distribution, the data must represent that data in a verbose but compact form—that is, we want to ensure that each of the traits on features that we are attempting to learn, is represented in similar quantities the way in which they would be generated.











How to do it...

Two predominant sets of class of techniques to fix imbalance are as follows:

  • Sampling techniques
  • Ensemble techniques

These techniques focus on sampling the data in a constructive or destructive way to achieve a better balanced distribution or working on the learning...