Prototypical networks are yet another simple, efficient, and popular learning algorithm. Like siamese networks, they try to learn the metric space to perform classification.
The basic idea of the prototypical network is to create a prototypical representation of each class and classify a query point (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:
We have three classes (lion, elephant, and dog). Now we need to create a prototypical representation for each of these three ...
