Random Erasing Data Augmentation

In this paper, we introduce Random Erasing, a new data augmentation method for training the convolutional neural network (CNN). In training, Random Erasing randomly selects a rectangle region in an image and erases its pixels with random values. In this process, training images with various levels of occlusion are generated, which reduces the risk of over-fitting and makes the model robust to occlusion. Random Erasing is parameter learning free, easy to implement, and can be integrated with most of the CNN-based recognition models. Albeit simple, Random Erasing is complementary to commonly used data augmentation techniques such as random cropping and flipping, and yields consistent improvement over strong…

• NN
  National Natural Science Foundation of China

  Awards: 61571188, U1705286, 61572409, 61806172, 61876159
• NK
  National Key Research and Development Program of China

  Award: 2018YFC0831402