Model-Agnostic Meta-Learning (MAML) is a popular gradient-based meta-learning framework that tries to find an optimal initialization to minimize the expected loss across all tasks during meta-training. However, it inherently assumes that the contribution of each instance/task to the meta-learner is equal. Therefore, it fails to address the problem of domain differences between base and novel classes in few-shot learning. In this work, we propose a novel and robust meta-learning algorithm, called RW-MAML, which learns to assign weights to training instances or tasks. We consider these weights to be hyper-parameters. Hence, we iteratively optimize the weights using a small set of validation tasks and an online approximation in a \emph{bi-bi-level} optimization framework, in contrast to the standard bi-level optimization in MAML. Therefore, we investigate a practical evaluation setting to demonstrate the scalability of our RW-MAML in two scenarios: (1) out-of-distribution tasks and (2) noisy labels in the meta-training stage. Extensive experiments on synthetic and real-world datasets demonstrate that our framework efficiently mitigates the effects of "unwanted" instances, showing that our proposed technique significantly outperforms state-of-the-art robust meta-learning methods.

本文提出了一种新颖的元学习算法NestedMAML，该算法可以学习分配给每个训练任务或实例的权重，并在元训练阶段应用，从而有效地缓解了不想要的任务或实例的影响，比现有的元学习算法都更鲁棒。

一个嵌套双层优化框架用于鲁棒性少样本学习