新算法让小样本学习模型更快适应新任务，提升泛化能力。
Domain-Generalization to Improve Learning in Meta-Learning Algorithms
- 双层优化中融合梯度匹配与平滑性正则，增强适应性
- 在基准数据集上准确率超越现有方法，泛化性能更优
- 适合少样本快速适配场景，代码开源便于复现
本文提出一种新型元学习算法DGS-MAML，旨在在训练数据有限的情况下实现跨任务的强泛化能力。该方法在双层优化框架中结合梯度匹配与平滑性感知最小化，提升模型的适应性和鲁棒性。通过PAC-Bayes理论分析和收敛性保证验证其有效性。在多个基准数据集上的实验表明，DGS-MAML在准确率和泛化性能上均优于现有方法。该方法特别适用于少样本学习和快速适应场景，源代码已公开于GitHub。
原文摘要 · Abstract (English)
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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