Meta-learning has emerged as a powerful approach for leveraging knowledge from previous tasks to solve new tasks. The mainstream methods focus on training a well-generalized model initialization, which is then adapted to different tasks with limited data and updates. However, it pushes the model overfitting on the training tasks. Previous methods mainly attributed this to the lack of data and used augmentations to address this issue, but they were limited by sufficient training and effective augmentation strategies. In this work, we focus on the more fundamental ``learning to learn'' strategy of meta-learning to explore what causes errors and how to eliminate these errors without changing the environment. Specifically, we first rethink the algorithmic procedure of meta-learning from a ``learning'' lens. Through theoretical and empirical analyses, we find that (i) this paradigm faces the risk of both overfitting and underfitting and (ii) the model adapted to different tasks promote each other where the effect is stronger if the tasks are more similar. Based on this insight, we propose using task relations to calibrate the optimization process of meta-learning and propose a plug-and-play method called Task Relation Learner (TRLearner) to achieve this goal. Specifically, it first obtains task relation matrices from the extracted task-specific meta-data. Then, it uses the obtained matrices with relation-aware consistency regularization to guide optimization. Extensive theoretical and empirical analyses demonstrate the effectiveness of TRLearner.

本研究解决了元学习中模型在训练任务上过拟合的问题。通过重新审视元学习的算法过程，提出了一种基于任务关系的优化校准方法——任务关系学习器（TRLearner），该方法利用任务特定的元数据来指导优化，实现了更好的泛化能力。实验和理论分析表明，TRLearner在减少过拟合和提高任务适应性方面表现出色。

从学习的视角重新思考元学习