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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

Evaluating Your First GAN

The evaluation of Generative Adversarial Networks (GANs) is a very active and exciting research field in generative models for generative models. Evaluation is the procedure through which we estimate the quality of our model and the samples produced with it. In this chapter, you will learn how to use different methods to evaluate the quality and variety of the GAN samples you produced in Chapter 3, Implementing your First GAN.

You will learn about the challenges involved in evaluating GAN samples. You will understand and learn to implement metrics for image quality. You will learn about using the birthday paradox to evaluate sample variety.

The following topics will be covered in this chapter:

  • The evaluation of GANs
  • Qualitative methods
  • Quantitative methods
  • GANs and the birthday paradox
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