  Modern deep networks can be better generalized when trained with noisy
samples and regularization techniques. Mixup and CutMix have been proven to be
effective for data augmentation to help avoid overfitting. Previous Mixup-based
methods linearly combine images and labels to generate additional training
data. However, this is problematic if the object does not occupy the whole
image as we demonstrate in Figure 1. Correctly assigning the label weights is
hard even for human beings and there is no clear criterion to measure it. To
tackle this problem, in this paper, we propose LUMix, which models such
uncertainty by adding label perturbation during training. LUMix is simple as it
can be implemented in just a few lines of code and can be universally applied
to any deep networks \eg CNNs and Vision Transformers, with minimal
computational cost. Extensive experiments show that our LUMix can consistently
boost the performance for networks with a wide range of diversity and capacity
on ImageNet, \eg $+0.7\%$ for a small model DeiT-S and $+0.6\%$ for a large
variant XCiT-L. We also demonstrate that LUMix can lead to better robustness
when evaluated on ImageNet-O and ImageNet-A. The source code can be found
\href{https://github.com/kevin-ssy/LUMix}{here}
