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

Introducing cGANs for face aging

So far, we have implemented different GAN networks for different use cases. Conditional GANs extend the idea of vanilla GANs and allow us to control the output of the generator network. Face aging is all about changing the age of a person's face without changing their identity. In most other models (including GANs), the appearance or identity of a person is lost by 50% because facial expressions and facial accessories, such as sunglasses or beards, are not taken into account. Age-cGANs consider all of these attributes. In this section, we will explore cGANs for face aging.

Understanding cGANs

cGANs are a type of GAN that are conditioned on some extra information. We feed the extra information...

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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.
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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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Generative AI with Python and TensorFlow 2

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