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 ...
