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

Summary

In this chapter, we were introduced to Age Conditional Generative Adversarial Networks (Age-cGANs). We then studied the architecture of Age-cGANs. After that, we learned how to set up our project and looked at the Keras implementation of Age-cGAN. Then we trained Age-cGAN on a wiki-cropped dataset, and went through all three stages of the Age-cGAN network. Finally, we discussed practical applications of Age-cGANs.

In the next chapter, we will be generating anime characters using another variant of GANs: DCGANs.

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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)

Hands-On Generative Adversarial Networks with PyTorch 1.x

Hands-On Generative Adversarial Networks with Keras

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