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.

本文通过应用神经科学中的一种良好的方法 - 表示相似性分析 (RSA)来研究模型无关元学习中的模型表示。虽然一部分分析支持特征重用是主导因素的一般结果，但我们也揭示了对其结论的反驳。指出对接近输入层的层的相似性增加起源于学习任务本身而不是模型。此外，内部梯度步骤后的表示具有比元训练期间的变化更广泛的变化。

探究模型不可知元学习中的表示相似性