“Prototypical Networks for Few-Shot Learning”, 2017-03-15 (; backlinks; similar):
We propose prototypical networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each new class.
Prototypical networks learn a metric space in which classification can be performed by computing distances to prototype representations of each class. Compared to recent approaches for few-shot learning, they reflect a simpler inductive bias that is beneficial in this limited-data regime, and achieve excellent results.
We provide an analysis showing that some simple design decisions can yield substantial improvements over recent approaches involving complicated architectural choices and meta-learning.
We further extend prototypical networks to zero-shot learning and achieve state-of-the-art results on the CU-Birds dataset.