Domain-Generalization to Improve Learning in Meta-Learning Algorithms
It addresses the challenge of few-shot learning and quick adaptation for meta-learning algorithms, but appears incremental as it builds on existing techniques like MAML and sharpness-aware minimization.
The paper tackles the problem of improving generalization across tasks in meta-learning with limited data by introducing DGS-MAML, which combines gradient matching and sharpness-aware minimization, and reports that it outperforms existing methods in accuracy and generalization on benchmark datasets.
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.