  Meta-learning algorithms produce feature extractors which achieve
state-of-the-art performance on few-shot classification. While the literature
is rich with meta-learning methods, little is known about why the resulting
feature extractors perform so well. We develop a better understanding of the
underlying mechanics of meta-learning and the difference between models trained
using meta-learning and models which are trained classically. In doing so, we
introduce and verify several hypotheses for why meta-learned models perform
better. Furthermore, we develop a regularizer which boosts the performance of
standard training routines for few-shot classification. In many cases, our
routine outperforms meta-learning while simultaneously running an order of
magnitude faster.
