You're reading from Hands-On Generative Adversarial Networks with Keras

Product typeBook

Published inMay 2019

Reading LevelIntermediate

PublisherPackt

ISBN-139781789538205

Edition1st Edition

Languages

Python

Tools

Keras

Concepts

Neural Networks

Author (1)

Rafael Valle

Summary

In this chapter, we investigated numerical properties of samples produced with adversarial methods, especially Generative Adversarial Networks. We showed that fake samples have properties that are barely noticed within visuals of samples, namely the fact that, due to stochastic gradient descent and the requirements of differentiability, fake samples smoothly approximate the dominating modes of the distribution. We analyzed statistical measures of divergence between real data and other data, and the results showed that even in simple cases – for instance, distribution of pixel intensities – the divergence between training data and fake data is large with respect to test data.

Although not common practice, one could possibly circumvent the difference in support between the real and fake data by training Generators that explicitly sample a distribution that...

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Hands-On Generative Adversarial Networks with Keras

Published in: May 2019Publisher: PacktISBN-13: 9781789538205

Author (1)

Rafael Valle

Rafael Valle is a research scientist at NVIDIA focusing on audio applications. He has years of experience developing high performance machine learning models for data/audio analysis, synthesis and machine improvisation with formal specifications. Dr. Valle was the first to generate speech samples from scratch with GANs and to show that simple yet efficient techniques can be used to identify GAN samples. He holds an Interdisciplinary PhD in Machine Listening and Improvisation from UC Berkeley, a Masters degree in Computer Music from the MH-Stuttgart in Germany and a Bachelors degree in Orchestral Conducting from UFRJ in Brazil.
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Author (1)

Hands-On Generative Adversarial Networks with PyTorch 1.x

Generative Adversarial Networks Projects

Hands-On Image Generation with TensorFlow

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Generative Adversarial Networks Cookbook

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