mlsanityData to 7 Oct 2026

Random Erasing Data Augmentation

arXiv 1708.04896 ↗First posted 16 Aug 2017cs.CVcited by 1,235

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Cited by 23 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

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.

Key facts. Cited by 1,235 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Aug 2017.

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Citing papers per year: 2021: 246; 2022: 267; 2023: 256; 2024: 235; 2025: 136; 2026: 95.

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