Domain-Generalization to Improve Learning in Meta-Learning Algorithms

• URL: http://arxiv.org/abs/2508.09418v1
• Date: Wed, 13 Aug 2025 01:30:11 GMT
• Title: Domain-Generalization to Improve Learning in Meta-Learning Algorithms
• Authors: Usman Anjum, Chris Stockman, Cat Luong, Justin Zhan,
• Abstract summary: Domain Generalization Sharpness-Aware Minimization Model-Agnostic Meta-Learning (DGS-MAML)<n>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.<n> Experimental results on benchmark datasets show that DGS-MAML outperforms existing approaches in terms of accuracy and generalization.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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