Boosting Data Augmentation with Stochastic Weight Averaging
本文提出将随机权重平均（SWA）应用于分类任务，作为无需重复训练运行的集成技术替代方案。研究通过以奥恩斯坦 - 乌伦贝克过程近似训练末尾的随机轨迹来分析 SWA，并证明在无限宽度极限下，基于增强数据的 SWA 提供了超越单纯性能提升预期的协变性增强效果。该结论已通过涵盖图像和图分类、包含离散与连续对称性的多种模型的广泛数值实验得到验证。
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On September 7, 2026, arXiv cs.LG published a study indicating that introducing the Stochastic Weight Averaging (SWA) strategy can significantly improve the performance of data augmentation. The study proposed using the average of multiple weights of the model during training as the final predictive model, rather than only the weight from the last step. Experiments showed that this method effectively alleviates overfitting and improves the model’s generalization ability, especially suitable for data augmentation tasks in small-sample scenarios.
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