In the metric-based meta learning setting, we will learn the appropriate metric space. Let's say we want to learn the similarity between two images. In the metric-based setting, we use a simple neural network that extracts the features from two images and finds the similarity by computing the distance between features of these two images. This approach is widely used in a few-shot learning setting where we don't have many data points. In the upcoming chapters, we will learn about metric-based learning algorithms such as Siamese networks, prototypical networks, and relation networks.