  Data augmentation (DA) is an essential technique for training
state-of-the-art deep learning systems. In this paper, we empirically show data
augmentation might introduce noisy augmented examples and consequently hurt the
performance on unaugmented data during inference. To alleviate this issue, we
propose a simple yet highly effective approach, dubbed \emph{KeepAugment}, to
increase augmented images fidelity. The idea is first to use the saliency map
to detect important regions on the original images and then preserve these
informative regions during augmentation. This information-preserving strategy
allows us to generate more faithful training examples. Empirically, we
demonstrate our method significantly improves on a number of prior art data
augmentation schemes, e.g. AutoAugment, Cutout, random erasing, achieving
promising results on image classification, semi-supervised image
classification, multi-view multi-camera tracking and object detection.
