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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 FREE CHAPTER 2. 3D-GAN - Generating Shapes Using GANs 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

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 rrothe@vision.ee.ethz.ch.

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...

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