# Cut-Thumbnail: A Novel Data Augmentation for Convolutional Neural Network Source: [Cut-Thumbnail: A Novel Data Augmentation for Convolutional Neural Network]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:40:16.064Z Authors: Tianshu Xie, Xuan Cheng, Minghui Liu, Jiali Deng, Xiaomin Wang, Ming Liu Published: 2021-03-09T10:45:55.000Z ## Abstract In this paper, we propose a novel data augmentation strategy named Cut-Thumbnail, that aims to improve the shape bias of the network. We reduce an image to a certain size and replace the random region of the original image with the reduced image. The generated image not only retains most of the original image information but also has global information in the reduced image. We call the reduced image as thumbnail. Furthermore, we find that the idea of thumbnail can be perfectly integrated with Mixed Sample Data Augmentation, so we put one image's thumbnail on another image while the ground truth labels are also mixed, making great achievements on various computer vision tasks. Extensive experiments show that Cut-Thumbnail works better than state-of-the-art augmentation strategies across classification, fine-grained image classification, and object detection. On ImageNet classification, ResNet-50 architecture with our method achieves 79.21\\% accuracy, which is more than 2.8\\% improvement on the baseline. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()