  Data augmentation (DA) is a widely used technique for enhancing the training
of deep neural networks. Recent DA techniques which achieve state-of-the-art
performance always meet the need for diversity in augmented training samples.
However, an augmentation strategy that has a high diversity usually introduces
out-of-distribution (OOD) augmented samples and these samples consequently
impair the performance. To alleviate this issue, we propose ReSmooth, a
framework that firstly detects OOD samples in augmented samples and then
leverages them. To be specific, we first use a Gaussian mixture model to fit
the loss distribution of both the original and augmented samples and
accordingly split these samples into in-distribution (ID) samples and OOD
samples. Then we start a new training where ID and OOD samples are incorporated
with different smooth labels. By treating ID samples and OOD samples unequally,
we can make better use of the diverse augmented data. Further, we incorporate
our ReSmooth framework with negative data augmentation strategies. By properly
handling their intentionally created OOD samples, the classification
performance of negative data augmentations is largely ameliorated. Experiments
on several classification benchmarks show that ReSmooth can be easily extended
to existing augmentation strategies (such as RandAugment, rotate, and jigsaw)
and improve on them. Our code is available at
https://github.com/Chenyang4/ReSmooth.
