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

Semi-prototypical networks

Now, we will see another interesting variant of prototypical networks called the semi-prototypical network. It deals with handling unlabeled examples. As we know, in the prototypical network, we compute the prototype of each class by taking the mean embedding of each class and then predict the class of query set by finding the distance between query points to the class prototypes.

Consider the case where our dataset contains some of the unlabeled data points: how do we compute the class prototypes of these unlabeled data points?

Let's say we have a support set,

where x is the feature and y is the label, and a query set,

. Along with these, we have one more set called the unlabeled set, R, where we have only unlabeled examples,


So, what can we do with this unlabeled set?

First, we will compute the class prototype with all the examples given in the support set. Next, we use soft k-means and assign the class for unlabeled examples in R—that is, we assign the class...