When MAML Can Adapt Fast and How to Assist When It Cannot

Understand

Model-Agnostic Meta-Learning (MAML) and its variants have achieved success in meta-learning tasks on many datasets and settings.

• On the other hand, we have just started to understand and analyze how they are able to adapt fast to new tasks.
• For example, one popular hypothesis is that the algorithms learn good representations for transfer, as in multi-task learning.
• In this work, we contribute by providing a series of empirical and theoretical studies, and discover several interesting yet previously unknown properties of the algorithm.