  Robust Model-Agnostic Meta-Learning (MAML) is usually adopted to train a
meta-model which may fast adapt to novel classes with only a few exemplars and
meanwhile remain robust to adversarial attacks. The conventional solution for
robust MAML is to introduce robustness-promoting regularization during
meta-training stage. With such a regularization, previous robust MAML methods
simply follow the typical MAML practice that the number of training shots
should match with the number of test shots to achieve an optimal adaptation
performance. However, although the robustness can be largely improved, previous
methods sacrifice clean accuracy a lot. In this paper, we observe that
introducing robustness-promoting regularization into MAML reduces the intrinsic
dimension of clean sample features, which results in a lower capacity of clean
representations. This may explain why the clean accuracy of previous robust
MAML methods drops severely. Based on this observation, we propose a simple
strategy, i.e., increasing the number of training shots, to mitigate the loss
of intrinsic dimension caused by robustness-promoting regularization. Though
simple, our method remarkably improves the clean accuracy of MAML without much
loss of robustness, producing a robust yet accurate model. Extensive
experiments demonstrate that our method outperforms prior arts in achieving a
better trade-off between accuracy and robustness. Besides, we observe that our
method is less sensitive to the number of fine-tuning steps during
meta-training, which allows for a reduced number of fine-tuning steps to
improve training efficiency.
