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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Free Chapter

Introduction to Generative Adversarial Networks

Introduction to Generative Adversarial Networks

Practical applications of GANs

The detailed architecture of a GAN

Variants of GANs

Advantages of GANs

Problems with training GANs

Solving stability problems when training GANs

3D-GAN - Generating Shapes Using GANs

3D-GAN - Generating Shapes Using GANs

Introduction to 3D-GANs

Setting up a project

Preparing the data

A Keras implementation of a 3D-GAN

Training a 3D-GAN

Hyperparameter optimization

Practical applications of 3D-GANs

Face Aging Using Conditional GAN

Face Aging Using Conditional GAN

Introducing cGANs for face aging

Setting up the project

Preparing the data

A Keras implementation of an Age-cGAN

Training the cGAN

Practical applications of Age-cGAN

Generating Anime Characters Using DCGANs

Generating Anime Characters Using DCGANs

Introducing to DCGANs

Setting up the project

Downloading and preparing the anime characters dataset

Implementing a DCGAN using Keras

Training the DCGAN

Practical applications of DCGAN

Using SRGANs to Generate Photo-Realistic Images

Using SRGANs to Generate Photo-Realistic Images

Introducing SRGANs

Setting up the project

Downloading the CelebA dataset

The Keras implementation of SRGAN

Training the SRGAN

Practical applications of SRGANs

StackGAN - Text to Photo-Realistic Image Synthesis

StackGAN - Text to Photo-Realistic Image Synthesis

Introduction to StackGAN

Architecture of StackGAN

Setting up the project

Data preparation

A Keras implementation of StackGAN

Training a StackGAN

Practical applications of StackGAN

CycleGAN - Turn Paintings into Photos

CycleGAN - Turn Paintings into Photos

An introduction to CycleGANs

Setting up the project

Downloading the dataset

Keras implementation of CycleGAN

Training the CycleGAN

Practical applications of CycleGANs

Further reading

Conditional GAN - Image-to-Image Translation Using Conditional Adversarial Networks

Conditional GAN - Image-to-Image Translation Using Conditional Adversarial Networks

Introducing Pix2pix

Setting up the project

Preparing the data

A Keras implementation of pix2pix

Training the pix2pix network

Practical applications of a pix2pix network

Predicting the Future of GANs

Predicting the Future of GANs

Our predictions about the future of GANs

Potential future applications of GANs

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Preparing the data

In this chapter, we will be using the Wiki-Cropped dataset, which contains more than 64, 328 images of various people's faces. The authors have also made a dataset available that contains only the cropped faces, so we don't need to crop faces.

The authors of the paper titled Deep expectation of real and apparent age from a single image without facial landmarks, which is available at https://www.vision.ee.ethz.ch/en/publications/papers/articles/eth_biwi_01299.pdf, have scraped these images from Wikipedia and made them available for academic purposes. If you intend to use the dataset for commercial purposes, contact the authors at [email protected].

You can manually download the dataset from the following link and place all the compressed files in the directory inside the Age-cGAN project at https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki...