  We propose a novel data augmentation method `GridMask' in this paper. It
utilizes information removal to achieve state-of-the-art results in a variety
of computer vision tasks. We analyze the requirement of information dropping.
Then we show limitation of existing information dropping algorithms and propose
our structured method, which is simple and yet very effective. It is based on
the deletion of regions of the input image. Our extensive experiments show that
our method outperforms the latest AutoAugment, which is way more
computationally expensive due to the use of reinforcement learning to find the
best policies. On the ImageNet dataset for recognition, COCO2017 object
detection, and on Cityscapes dataset for semantic segmentation, our method all
notably improves performance over baselines. The extensive experiments manifest
the effectiveness and generality of the new method.
