  In past years model-agnostic meta-learning (MAML) has been one of the most
promising approaches in meta-learning. It can be applied to different kinds of
problems, e.g., reinforcement learning, but also shows good results on few-shot
learning tasks. Besides their tremendous success in these tasks, it has still
not been fully revealed yet, why it works so well. Recent work proposes that
MAML rather reuses features than rapidly learns. In this paper, we want to
inspire a deeper understanding of this question by analyzing MAML's
representation. We apply representation similarity analysis (RSA), a
well-established method in neuroscience, to the few-shot learning instantiation
of MAML. Although some part of our analysis supports their general results that
feature reuse is predominant, we also reveal arguments against their
conclusion. The similarity-increase of layers closer to the input layers arises
from the learning task itself and not from the model. In addition, the
representations after inner gradient steps make a broader change to the
representation than the changes during meta-training.
