Book Image

Hands-On One-shot Learning with Python

By : Shruti Jadon, Ankush Garg
Book Image

Hands-On One-shot Learning with Python

By: Shruti Jadon, Ankush Garg

Overview of this book

One-shot learning has been an active field of research for scientists trying to develop a cognitive machine that mimics human learning. With this book, you'll explore key approaches to one-shot learning, such as metrics-based, model-based, and optimization-based techniques, all with the help of practical examples. Hands-On One-shot Learning with Python will guide you through the exploration and design of deep learning models that can obtain information about an object from one or just a few training samples. The book begins with an overview of deep learning and one-shot learning and then introduces you to the different methods you can use to achieve it, such as deep learning architectures and probabilistic models. Once you've got to grips with the core principles, you'll explore real-world examples and implementations of one-shot learning using PyTorch 1.x on datasets such as Omniglot and MiniImageNet. Finally, you'll explore generative modeling-based methods and discover the key considerations for building systems that exhibit human-level intelligence. By the end of this book, you'll be well-versed with the different one- and few-shot learning methods and be able to use them to build your own deep learning models.

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Table of Contents (11 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
1
Section 1: One-shot Learning Introduction
Section 1: One-shot Learning Introduction
Free Chapter
2
Introduction to One-shot Learning
Introduction to One-shot Learning
Technical requirements
The human brain – overview
Machine learning – historical overview
One-shot learning – overview
Setting up your environment
Coding exercise
Summary
Questions
3
Section 2: Deep Learning Architectures
Section 2: Deep Learning Architectures
4
Metrics-Based Methods
Metrics-Based Methods
Technical requirements
Parametric methods – an overview
Understanding Siamese networks
Understanding matching networks
Coding exercise
Summary
Questions
Further reading
5
Model-Based Methods
Model-Based Methods
Technical requirements
Understanding Neural Turing Machines
Memory-augmented neural networks
Understanding meta networks
Coding exercises
Summary
Questions
Further reading
6
Optimization-Based Methods
Optimization-Based Methods
Technical requirements
Overview of gradient descent
Understanding model-agnostic meta-learning
Understanding LSTM meta-learner
Exercises
Summary
Questions
Further reading
7
Section 3: Other Methods and Conclusion
Section 3: Other Methods and Conclusion
8
Generative Modeling-Based Methods
Generative Modeling-Based Methods
Technical requirements
Overview of Bayesian learning
Understanding directed graphical models
Overview of probabilistic methods
Bayesian program learning
Discriminative k-shot learning
Summary
Further reading
9
Conclusions and Other Approaches
Conclusions and Other Approaches
Recent advancements
Related fields
Applications
Further reading
10
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Discriminative k-shot learning

A very common approach for k-shot learning is to train a large model with a related task for which we have a large dataset. This model is then fine-tuned with the k-shot specific task. Hence, the knowledge from the large dataset is distilled into the model, which augments the learning of new related tasks from just a few examples. In 2003, Bakker and Heskes introduced a probabilistic model for k-shot learning where all of the tasks share a common feature extractor but have a respective linear classifier with just a few task-specific parameters.

The probabilistic method to k-shot learning discussed here is very similar to the one introduced by Bakker and Heskes. This method solves the classification task (for images) by learning a probabilistic model from very little data. The idea is to use a powerful neural network that learns robust features from...

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