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)

Introduction to StackGAN

A StackGAN is named as such because it has two GANs that are stacked together to form a network that is capable of generating high-resolution images. It has two stages, Stage-I and Stage-II. The Stage-I network generates low-resolution images with basic colors and rough sketches, conditioned on a text embedding, while the Stage-II network takes the image generated by the Stage-I network and generates a high-resolution image that is conditioned on a text embedding. Basically, the second network corrects defects and adds compelling details, yielding a more realistic high-resolution image.

We can compare a StackGAN network to the work of a painter. As a painter starts working, they draw primitive shapes such as lines, circles, and rectangles. Then, they try to fill in the colors. As the painting progresses, more and more detail is added. In a StackGAN, Stage...