  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.
