Exploring the Similarity of Representations in Model-Agnostic Meta-Learning
This provides incremental insights into the mechanisms of MAML for researchers in meta-learning, though it does not propose a new method or broad SOTA improvements.
The paper investigates why model-agnostic meta-learning (MAML) works well by analyzing its representations using representation similarity analysis (RSA) on few-shot learning tasks, finding that feature reuse is predominant but that similarity increases in input layers stem from the learning task itself and that inner gradient steps cause broader representation changes than meta-training.
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.