  Deploying machine learning systems in the real world requires both high
accuracy on clean data and robustness to naturally occurring corruptions. While
architectural advances have led to improved accuracy, building robust models
remains challenging. Prior work has argued that there is an inherent trade-off
between robustness and accuracy, which is exemplified by standard data augment
techniques such as Cutout, which improves clean accuracy but not robustness,
and additive Gaussian noise, which improves robustness but hurts accuracy. To
overcome this trade-off, we introduce Patch Gaussian, a simple augmentation
scheme that adds noise to randomly selected patches in an input image. Models
trained with Patch Gaussian achieve state of the art on the CIFAR-10 and
ImageNetCommon Corruptions benchmarks while also improving accuracy on clean
data. We find that this augmentation leads to reduced sensitivity to high
frequency noise(similar to Gaussian) while retaining the ability to take
advantage of relevant high frequency information in the image (similar to
Cutout). Finally, we show that Patch Gaussian can be used in conjunction with
other regularization methods and data augmentation policies such as
AutoAugment, and improves performance on the COCO object detection benchmark.
