  Data augmentation plays a crucial role in enhancing the robustness and
performance of machine learning models across various domains. In this study,
we introduce a novel mixed-sample data augmentation method called RandoMix.
RandoMix is specifically designed to simultaneously address robustness and
diversity challenges. It leverages a combination of linear and mask-mixed
modes, introducing flexibility in candidate selection and weight adjustments.
We evaluate the effectiveness of RandoMix on diverse datasets, including
CIFAR-10/100, Tiny-ImageNet, ImageNet, and Google Speech Commands. Our results
demonstrate its superior performance compared to existing techniques such as
Mixup, CutMix, Fmix, and ResizeMix. Notably, RandoMix excels in enhancing model
robustness against adversarial noise, natural noise, and sample occlusion. The
comprehensive experimental results and insights into parameter tuning
underscore the potential of RandoMix as a versatile and effective data
augmentation method. Moreover, it seamlessly integrates into the training
pipeline.
