📄 中文摘要
Sharpness-Aware Minimization (SAM) 算法通过关注模型损失函数的平滑性，显著提升了深度学习模型的泛化能力。传统的优化方法往往只关注最小化训练损失，而 SAM 则引入了对损失函数在参数空间中形状的敏感性考量。通过在训练过程中动态调整学习率，SAM 能够有效避免过拟合，并提升模型在未见数据上的表现。该算法在多个基准数据集上的实验结果表明，使用 SAM 优化的模型在准确性和鲁棒性方面均优于传统优化方法，展示了其在深度学习领域的广泛应用潜力。
利用 SAM 优化深度学习模型
出处: Optimizing Deep Learning Models with SAM
发布: 2026年2月24日
📄 English Summary
Optimizing Deep Learning Models with SAM
The Sharpness-Aware Minimization (SAM) algorithm significantly enhances the generalization capabilities of deep learning models by focusing on the smoothness of the model's loss function. Traditional optimization methods primarily aim to minimize training loss, while SAM introduces a sensitivity consideration regarding the shape of the loss function in parameter space. By dynamically adjusting the learning rate during training, SAM effectively mitigates overfitting and improves model performance on unseen data. Experimental results across various benchmark datasets demonstrate that models optimized with SAM outperform those optimized with traditional methods in terms of accuracy and robustness, showcasing its broad application potential in the deep learning field.