  Meta learning methods have found success when applied to few shot
classification problems, in which they quickly adapt to a small number of
labeled examples. Prototypical representations, each representing a particular
class, have been of particular importance in this setting, as they provide a
compact form to convey information learned from the labeled examples. However,
these prototypes are just one method of representing this information, and they
are narrow in their scope and ability to classify unseen examples. We propose
the implementation of contextualizers, which are generalizable prototypes that
adapt to given examples and play a larger role in classification for
gradient-based models. We demonstrate how to equip meta learning methods with
contextualizers and show that their use can significantly boost performance on
a range of few shot learning datasets. We also present figures of merit
demonstrating the potential benefits of contextualizers, along with analysis of
how models make use of them. Our approach is particularly apt for low-data
environments where it is difficult to update parameters without overfitting.
Our implementation and instructions to reproduce the experiments are available
at https://github.com/naveace/proto-context.
