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arXiv · 2508.09418

Domain-Generalization to Improve Learning in Meta-Learning Algorithms

Usman Anjum·Chris Stockman·Cat Luong·Justin Zhan
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Abstract

This paper introduces Domain Generalization Sharpness-Aware Minimization Model-Agnostic Meta-Learning (DGS-MAML), a novel meta-learning algorithm designed to generalize across tasks with limited training data. DGS-MAML combines gradient matching with sharpness-aware minimization in a bi-level optimization framework to enhance model adaptability and robustness. We support our method with theoretical analysis using PAC-Bayes and convergence guarantees. Experimental results on benchmark datasets show that DGS-MAML outperforms existing approaches in terms of accuracy and generalization. The proposed method is particularly useful for scenarios requiring few-shot learning and quick adaptation, and the source code is publicly available at GitHub.

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Usman Anjum, Chris Stockman, Cat Luong, Justin Zhan. 2025-08-13. Domain-Generalization to Improve Learning in Meta-Learning Algorithms. https://arxiv.org/abs/2508.09418

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