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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

Reptile

The Reptile algorithm has been proposed as an improvement to MAML by OpenAI. It's simple and easier to implement. We know that, in MAML, we calculate second order derivatives—that is, the gradient of gradients. But computationally, this isn't an efficient task. So, OpenAI came up with an improvement over MAML called Reptile. The algorithm of Reptile is very simple. Sample some n number of tasks and run Stochastic Gradient Descent (SGD) for fewer iterations on each of the sampled tasks and then update our model parameter in a direction that's common to all of the tasks. Since we're performing SGD for fewer iterations on each task, it indirectly implies we're calculating the second order derivative over the loss. Unlike MAML, it's computationally effective as we're not calculating the second order derivative directly nor unrolling...

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