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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
2. Deep Learning Basics and Environment Setup FREE CHAPTER 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

Generative models

We will look into the various approaches of generative models in the following sections.

Autoregressive models

Autoregressive models estimate the conditional distribution of some data , given some other values of y. For example, in image synthesis, it estimates the conditional distribution of pixels given surrounding or previous pixels; in audio synthesis, it estimates the conditional distribution of audio samples given previous audio samples and spectrograms.

In its simplest linear form, with dependency on the previous time-step only and time-invariant bias term, an autoregressive model can be defined with the following equation:

is a constant term that represents the model's bias, represents the...

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