- A siamese network is a special type of neural network, and it is one of the simplest and most commonly used one-shot learning algorithms. Siamese networks basically consist of two symmetrical neural networks that share the same weights and architecture and are joined together at the end using an energy function, E.
- The contrastive loss function can be expressed as follows:
In the preceding equation, the value of Y is the true label, which will be 1 when the two input values are similar and 0 if the two input values are dissimilar, and E is our energy function, which can be any distance measure. The term margin is used to hold the constraint; that is, when two input values are dissimilar and if their distance is greater than a margin, then they do not incur a loss.
The energy function tells us how similar the two inputs are. It is basically any similarity measure, such as Euclidean distance and cosine similarity.
The input to the siamese networks should be in pairs, (X1,X2), along with their binary label, Y ∈ (0, 1),stating whether the input pairs are genuine pairs (the same) orimposite pairs (different).
The applications of siamese networks are endless; they've been stacked with various architectures for performing various tasks, such as human action recognition, scene change detection, and machine translation.
Hands-On Meta Learning with Python
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Hands-On Meta Learning with Python
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Overview of this book
Meta learning is an exciting research trend in machine learning, which enables a model to understand the learning process. Unlike other ML paradigms, with meta learning you can learn from small datasets faster.
Hands-On Meta Learning with Python starts by explaining the fundamentals of meta learning and helps you understand the concept of learning to learn. You will delve into various one-shot learning algorithms, like siamese, prototypical, relation and memory-augmented networks by implementing them in TensorFlow and Keras. As you make your way through the book, you will dive into state-of-the-art meta learning algorithms such as MAML, Reptile, and CAML. You will then explore how to learn quickly with Meta-SGD and discover how you can perform unsupervised learning using meta learning with CACTUs. In the concluding chapters, you will work through recent trends in meta learning such as adversarial meta learning, task agnostic meta learning, and meta imitation learning.
By the end of this book, you will be familiar with state-of-the-art meta learning algorithms and able to enable human-like cognition for your machine learning models.
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Current Title:
Hands-On Meta Learning with Python
Table of Contents (17 chapters)
Title Page
Dedication
About Packt
Contributors
Preface
Free Chapter
Introduction to Meta Learning
Face and Audio Recognition Using Siamese Networks
Prototypical Networks and Their Variants
Relation and Matching Networks Using TensorFlow
Memory-Augmented Neural Networks
MAML and Its Variants
Meta-SGD and Reptile
Gradient Agreement as an Optimization Objective
Recent Advancements and Next Steps
Assessments
Other Books You May Enjoy
Index
Customer Reviews