Data augmentation techniques play an important role in enhancing the performance of deep learning models. Despite their proven benefits in computer vision tasks, their application in the other domains remains limited. This paper proposes a Mixup regularization scheme, referred to as UMAP Mixup, designed for "on-manifold" automated data augmentation for deep learning predictive models. The proposed approach ensures that the Mixup operations result in synthesized samples that lie on the data manifold of the features and labels by utilizing a dimensionality reduction technique known as uniform manifold approximation and projection. Evaluations across diverse regression tasks show that UMAP Mixup is competitive with or outperforms other Mixup variants, show promise for its potential as an effective tool for enhancing the generalization performance of deep learning models.

深度学习模型的数据增强技术在提高性能方面发挥重要作用。本文提出了一种叫做UMAP Mixup的混合正则化方案，用于深度学习预测模型的“在流形上”自动数据增强。该方法通过利用一种称为统一流形逼近和投影的降维技术，确保Mixup操作生成的合成样本位于特征和标签的数据流形上。对多样的回归任务的评估显示，UMAP Mixup在与其他Mixup变种的竞争中表现出色，显示出改进深度学习模型泛化性能的潜力。

流形增强：将混合正则化与UMAP结合