  Mixup data augmentation approaches have been applied for various tasks of
deep learning to improve the generalization ability of deep neural networks.
Some existing approaches CutMix, SaliencyMix, etc. randomly replace a patch in
one image with patches from another to generate the mixed image. Similarly, the
corresponding labels are linearly combined by a fixed ratio $\lambda$ by l. The
objects in two images may be overlapped during the mixing process, so some
semantic information is corrupted in the mixed samples. In this case, the mixed
image does not match the mixed label information. Besides, such a label may
mislead the deep learning model training, which results in poor performance. To
solve this problem, we proposed a novel approach named SUMix to learn the
mixing ratio as well as the uncertainty for the mixed samples during the
training process. First, we design a learnable similarity function to compute
an accurate mix ratio. Second, an approach is investigated as a regularized
term to model the uncertainty of the mixed samples. We conduct experiments on
five image benchmarks, and extensive experimental results imply that our method
is capable of improving the performance of classifiers with different
cutting-based mixup approaches. The source code is available at
https://github.com/JinXins/SUMix.
