  Recent years have witnessed an abundance of new publications and approaches
on meta-learning. This community-wide enthusiasm has sparked great insights but
has also created a plethora of seemingly different frameworks, which can be
hard to compare and evaluate. In this paper, we aim to provide a principled,
unifying framework by revisiting and strengthening the connection between
meta-learning and traditional supervised learning. By treating pairs of
task-specific data sets and target models as (feature, label) samples, we can
reduce many meta-learning algorithms to instances of supervised learning. This
view not only unifies meta-learning into an intuitive and practical framework
but also allows us to transfer insights from supervised learning directly to
improve meta-learning. For example, we obtain a better understanding of
generalization properties, and we can readily transfer well-understood
techniques, such as model ensemble, pre-training, joint training, data
augmentation, and even nearest neighbor based methods. We provide an intuitive
analogy of these methods in the context of meta-learning and show that they
give rise to significant improvements in model performance on few-shot
learning.
