Approaches

Now that we have a grasp of the foundations of few-shot learning, we’ll take a look at some of the most common approaches to the solving few-shot problems.

Recall that the goal of few-shot learning is to be able to learn to solve a new machine learning task given only a few labeled examples. In few-shot learning problems, we are given a small set of labeled examples for each class we would like to predict (the support set), as well as a larger set of unlabeled examples (the query set). We tend to refer to few-shot learning tasks as \(K\)-way, \(N\)-shot classification tasks, where \(K\) is the number of classes we would like to predict, and \(N\) is the number of labeled examples we are given for each class.

When training a model to solve a few-shot learning task, we typically sample episodes from a large training dataset. An episode is a simulation of a few-shot learning task, where we sample \(K\) classes and \(N\) labeled examples for each class. Training a deep model by sampling few-shot learning episodes from a large training dataset is known as episodic training.

Here are the few-shot learning approaches covered in this tutorial:

1. Metric-based few-shot learning

2. Optimization-based few-shot learning