Reviews: Prototypical Networks for Few-shot Learning

The idea is to represent each class as its mean/prototype within a learned embedding space, and then recognising new classes via softmax over distances to the prototypes. The model is trained by randomly randomly sampling classes and instances per episode. This is appealing due to its simplicity and speed compared to other influential few-shot methodologies [21,28]. Some insights are given about the connection to mixture density estimation in the case of Bregman divergence-based distances, linear models, and matching networks. The same framework extends relatively straightforwardly to zero-shot learning by making the class prototype be the result of a learned mapping from meta-data like attributes to the prototype vector.