Prototypical Networks for Few-shot Learning
This addresses the problem of generalizing to new classes with limited data for machine learning practitioners, representing a novel method rather than an incremental improvement.
The paper tackles few-shot classification by proposing prototypical networks, which learn a metric space for classification based on distances to class prototypes, achieving excellent results and state-of-the-art performance on the CU-Birds dataset for zero-shot learning.
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.