Prototypical networks are yet another simple, efficient, few shot learning algorithm. Like siamese networks, a prototypical network tries to learn the metric space to perform classification. The basic idea of prototypical networks is to create a prototypical representation of each class and classify a query point (that is, a new point) based on the distance between the class prototype and the query point.
Let's say we have a support set comprising images of lions, elephants, and dogs, as shown in the following diagram:
So, we have three classes: {lion, elephant, dog}. Now we need to create a prototypical representation for each of these three class. How can we build the prototype of these three classes? First, we will learn the embeddings of each data point using an embedding function. The embedding function,
, can be any function that can be used to extract features. Since our input is an image, we can use the convolutional network as our embedding function, which will...
