Computer Science > Machine Learning
[Submitted on 18 Jul 2023 (v1), last revised 2 Jun 2024 (this version, v4)]
Title:Towards Task Sampler Learning for Meta-Learning
View PDF HTML (experimental)Abstract: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.
Submission history
From: Jingyao Wang [view email][v1] Tue, 18 Jul 2023 01:53:18 UTC (2,792 KB)
[v2] Tue, 5 Sep 2023 01:15:42 UTC (2,792 KB)
[v3] Thu, 29 Feb 2024 02:53:32 UTC (13,381 KB)
[v4] Sun, 2 Jun 2024 08:52:13 UTC (12,787 KB)
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