  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.
