ReSmooth: Detecting and Utilizing OOD Samples when Training with Data Augmentation. (arXiv:2205.12606v1 [cs.CV])

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 …

arxiv augmentation cv data training