Book Image

Hands-On Meta Learning with Python

By : Sudharsan Ravichandiran
Book Image

Hands-On Meta Learning with Python

By: Sudharsan Ravichandiran

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.
Table of Contents (17 chapters)
Title Page
About Packt

Gaussian prototypical network

Now, we will look at a variant of a prototypical network, called a Gaussian prototypical network. We just learned how a prototypical network learns the embeddings of the data points and how it builds the class prototype by taking the mean embeddings of each class and uses the class prototype for performing classification.

In a Gaussian prototypical network, along with generating embeddings for the data points, we add a confidence region around them, characterized by a Gaussian covariance matrix. Having a confidence region helps in characterizing the quality of individual data points and would be useful in the case of noisy and less homogeneous data.

So, in Gaussian prototypical networks, the output of the encoder will be embeddings, as well as the covariance matrix. Instead of using the full covariance matrix, we either include a radius or diagonal component from the covariance matrix along with the embeddings:

  • Radius component: If we use the radius component of...