Model-Agnostic Meta-Learning (MAML), a popular gradient-based meta-learning
framework, assumes that the contribution of each task or instance to the
meta-learner is equal. Hence, it fails to address the domain shift between base
and novel classes in few-shot learning. In this work, we propose a novel robust
meta-learning algorithm, NestedMAML, which learns to assign weights to training
tasks or instances. We consider weights as hyper-parameters and iteratively
optimize them using a small set of validation tasks set in a nested bi-level
optimization approach (in contrast to the standard bi-level optimization in
MAML). We then apply NestedMAML in the meta-training stage, which involves (1)
several tasks sampled from a distribution different from the meta-test task
distribution, or (2) some data samples with noisy labels. Extensive experiments
on synthetic and real-world datasets demonstrate that NestedMAML efficiently
mitigates the effects of "unwanted" tasks or instances, leading to significant
improvement over the 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。