You're reading from Generative Adversarial Networks Projects

Product typeBook

Published inJan 2019

Reading LevelIntermediate

PublisherPackt

ISBN-139781789136678

Edition1st Edition

Languages

Python

Tools

TensorFlow Keras

Concepts

Deep Learning

Author (1)

Kailash Ahirwar

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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Generative Adversarial Networks Projects

Published in: Jan 2019Publisher: PacktISBN-13: 9781789136678

Author (1)

Kailash Ahirwar

Kailash Ahirwar is a machine learning and deep learning enthusiast. He has worked in many areas of Artificial Intelligence (AI), ranging from natural language processing and computer vision to generative modeling using GANs. He is a co-founder and CTO of Mate Labs. He uses GANs to build different models, such as turning paintings into photos and controlling deep image synthesis with texture patches. He is super optimistic about AGI and believes that AI is going to be the workhorse of human evolution.
Read more about Kailash Ahirwar

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Author (1)

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