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Hands-On Generative Adversarial Networks with Keras

You're reading from   Hands-On Generative Adversarial Networks with Keras Your guide to implementing next-generation generative adversarial networks

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781789538205
Length 272 pages
Edition 1st Edition
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Author (1):
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Rafael Valle Rafael Valle
Author Profile Icon Rafael Valle
Rafael Valle
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Table of Contents (14) Chapters Close

Preface 1. Section 1: Introduction and Environment Setup FREE CHAPTER
2. Deep Learning Basics and Environment Setup 3. Introduction to Generative Models 4. Section 2: Training GANs
5. Implementing Your First GAN 6. Evaluating Your First GAN 7. Improving Your First GAN 8. Section 3: Application of GANs in Computer Vision, Natural Language Processing, and Audio
9. Progressive Growing of GANs 10. Generation of Discrete Sequences Using GANs 11. Text-to-Image Synthesis with GANs 12. TequilaGAN - Identifying GAN Samples 13. Whats next in GANs

GAN model architectures

The following section will discuss alternative GAN model architectures for the Discriminator and Generator.

ResNet GAN

In its simplest form, a ResNet network is a network with residual layers. A residual layer is a layer in which the layer input is added to the layer output. This connection from the layer input to the layer output is called a residual connection, as illustrated in the following diagram:

Multiple variations of the a ResNet layer. Copied from Identity Mappings in Deep Residual Networks (https://arxiv.org/abs/1603.05027)

Let's take a look at an implementation of a ResNet GAN that could be used for image denoising, image super resolution, image-to-image translation, audio denoising...

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