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Generative Adversarial Networks Projects

You're reading from   Generative Adversarial Networks Projects Build next-generation generative models using TensorFlow and Keras

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Product type Paperback
Published in Jan 2019
Publisher Packt
ISBN-13 9781789136678
Length 316 pages
Edition 1st Edition
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Author (1):
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Kailash Ahirwar Kailash Ahirwar
Author Profile Icon Kailash Ahirwar
Kailash Ahirwar
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Table of Contents (11) Chapters Close

Preface 1. Introduction to Generative Adversarial Networks 2. 3D-GAN - Generating Shapes Using GANs FREE CHAPTER 3. Face Aging Using Conditional GAN 4. Generating Anime Characters Using DCGANs 5. Using SRGANs to Generate Photo-Realistic Images 6. StackGAN - Text to Photo-Realistic Image Synthesis 7. CycleGAN - Turn Paintings into Photos 8. Conditional GAN - Image-to-Image Translation Using Conditional Adversarial Networks 9. Predicting the Future of GANs 10. Other Books You May Enjoy

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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