【论文阅读笔记】Random Erasing Data Augmentation 论文地址:random erasing 论文总结 本文的方法名为random erasing,是一种数据增强的方法。通过随机选择不同大小的方形区域,填充随机像素值,达到增加数据...不同遮挡级别的增强图像。 算法的实现如下图示:(1)有一个随机的概率ppp选择是否做random erasing...
2. Random Erasing 3. Random Erasing for Image Classification and Person Re-identification 4. Random Erasing for Object Detection 5. Comparison with Random Cropping * 目录结构与原文有差异 0.基础信息 论文地址: Random Erasing Data Augmentationarxiv.org/pdf/1708.04896.pdf 论文代码: Random-Erasing...
<Code is available at: https://github.com/zhunzhong07/Random-Erasing>【前几天与师兄交谈实验的时候,提了一下这篇论文,因为这篇论文的数据增强方式对于我的实验项目有所帮助,借此机会读一下这篇论文…
Random Erasing Data Augmentation(REA)是一种随机擦除的数据增广方法。简单而言就是在图像中随机选择一个区域,打上噪声mask。这个mask可以是黑块、灰块也可以是随机正太噪声。。该方法被证明在多个CNN架构和不同领域中可以提升模型的性能和应对遮挡的鲁棒性,并且与随机裁剪、随机水平翻转(还有正则化方法)具有一定的互...
实验结果显示，当在ResNet、ResNeXt和Wide Residual Networks等网络架构中应用随机擦除时，模型的测试性能得到了提升。通过比较不同填充方式，研究发现随机数填充效果最佳。总的来说，Random Erasing Data Augmentation的核心在于选择合适的擦除区域，通过随机像素替换增强模型的鲁棒性。文章详细描述了这种方法的...
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 ...
This code has the source code for the paper "Random Erasing Data Augmentation". If you find this code useful in your research, please consider citing: @inproceedings{zhong2020random, title={Random Erasing Data Augmentation}, author={Zhong, Zhun and Zheng, Liang and Kang, Guoliang and Li, Sha...
Random erasing uses rectangular areas to erase images, which improves model generalization performance, but there is too much loss of original image information. In this paper, we introduce a novel approach, the improved data augmentation method based on random walk algorithm, called random walk-base...
正确配置超参数，并与其他增强技术协调使用，是实现最佳效果的关键。务必结合具体任务和数据集特性，才能最大程度地发挥其潜力。参考文献：[1] Zhong Z, Zheng L, Kang G, et al. Random Erasing Data Augmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 2017, 34(7).
This code has the source code for the paper "Random Erasing Data Augmentation". If you find this code useful in your research, please consider citing: @inproceedings{zhong2020random, title={Random Erasing Data Augmentation}, author={Zhong, Zhun and Zheng, Liang and Kang, Guoliang and Li, Sha...