Meta-learning aims to learn general knowledge with diverse training tasks conducted from limited data, and then transfer it to new tasks. It is commonly believed that increasing task diversity will enhance the generalization ability of meta-learning models. However, this paper challenges this view through empirical and theoretical analysis. We obtain three conclusions: (i) there is no universal task sampling strategy that can guarantee the optimal performance of meta-learning models; (ii) over-constraining task diversity may incur the risk of under-fitting or over-fitting during training; and (iii) the generalization performance of meta-learning models are affected by task diversity, task entropy, and task difficulty. Based on this insight, we design a novel task sampler, called Adaptive Sampler (ASr). ASr is a plug-and-play module that can be integrated into any meta-learning framework. It dynamically adjusts task weights according to task diversity, task entropy, and task difficulty, thereby obtaining the optimal probability distribution for meta-training tasks. Finally, we conduct experiments on a series of benchmark datasets across various scenarios, and the results demonstrate that ASr has clear advantages.

翻译：元学习旨在通过有限数据构建的多样化训练任务学习通用知识，并将其迁移至新任务。传统观点认为增加任务多样性能够提升元学习模型的泛化能力，但本文通过实证与理论分析对此提出挑战。我们得出三个结论：(i) 不存在能保证元学习模型获得最优性能的通用任务采样策略；(ii) 过度强调任务多样性可能导致训练过程中的欠拟合或过拟合风险；(iii) 元学习模型的泛化性能受任务多样性、任务熵与任务难度共同影响。基于此洞见，我们设计了一种称为自适应采样器（ASr）的新型任务采样器。ASr作为即插即用模块可集成至任意元学习框架，能够根据任务多样性、任务熵与任务难度动态调整任务权重，从而获得元训练任务的最优概率分布。最后，我们在多种场景下的系列基准数据集上进行实验，结果表明ASr具有显著优势。