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)

Downloading and preparing the anime characters dataset

To train a DCGAN network, we need a dataset of anime characters containing cropped faces of the characters. There are multiple ways to collect a dataset. We can either use a publicly available dataset, or we can scrape one, as long as we don't violate the website's scraping policies. In this chapter, we will be scraping images for educational and demonstration purposes only. We have scraped images from pixiv.net using a crawler tool called gallery-dl. This is a command-line tool that can be used to download image collections from websites, such as pixiv.net, exhentai.org, danbooru.donmai.us, and more. It is available at the following link: https://github.com/mikf/gallery-dl.

Downloading the dataset

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