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Hands-On Meta Learning with Python

You're reading from   Hands-On Meta Learning with Python Meta learning using one-shot learning, MAML, Reptile, and Meta-SGD with TensorFlow

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781789534207
Length 226 pages
Edition 1st Edition
Languages
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Author (1):
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Sudharsan Ravichandiran Sudharsan Ravichandiran
Author Profile Icon Sudharsan Ravichandiran
Sudharsan Ravichandiran
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Table of Contents (12) Chapters Close

Preface 1. Introduction to Meta Learning 2. Face and Audio Recognition Using Siamese Networks FREE CHAPTER 3. Prototypical Networks and Their Variants 4. Relation and Matching Networks Using TensorFlow 5. Memory-Augmented Neural Networks 6. MAML and Its Variants 7. Meta-SGD and Reptile 8. Gradient Agreement as an Optimization Objective 9. Recent Advancements and Next Steps 10. Assessments 11. Other Books You May Enjoy

Prototypical Networks and Their Variants

In the last chapter, we learned what siamese networks are and how they are used to perform few-shot learning tasks. We also explored how to use siamese networks for performing face and audio recognition. In this chapter, we will look at another interesting few-shot learning algorithm called a prototypical network, which has the ability to generalize even to the class that is not present in a training set. We will start off with understanding what prototypical networks are, after which we will see how to perform a classification task in an omniglot dataset using prototypical network. We will then see different variants of prototypical networks, such as Gaussian prototypical networks and semi-prototypical networks.

In this chapter, you will learn about the following:

  • Prototypical networks
  • The algorithm of prototypical networks
  • Classification...
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