Random Erasing Data Augmentation
This addresses the issue of model robustness to occlusion for researchers and practitioners in computer vision, though it is incremental as it builds on existing data augmentation techniques.
The paper tackles the problem of overfitting in convolutional neural networks by introducing Random Erasing, a data augmentation method that randomly occludes parts of images during training, resulting in consistent improvements in tasks like image classification, object detection, and person re-identification.
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 baselines in image classification, object detection and person re-identification. Code is available at: https://github.com/zhunzhong07/Random-Erasing.