Abstract
In this paper, we introduce a novel data augmentation technique that combines the advantages of style augmentation and random erasing by selectively replacing image subregions with style-transferred patches. Our approach first applies a random style transfer to training images, then randomly substitutes selected areas of these images with patches derived from the style-transferred versions. This method is able to seamlessly accommodate a wide range of existing style transfer algorithms and can be readily integrated into diverse data augmentation pipelines. By incorporating our strategy, the training process becomes more robust and less prone to overfitting. Comparative experiments demonstrate that, relative to previous style augmentation methods, our technique achieves superior performance and faster convergence.
Abstract (translated)
在本文中，我们介绍了一种新颖的数据增强技术，该技术结合了样式增强和随机擦除的优点，通过选择性地用经过风格转换的补丁替换图像子区域来实现。我们的方法首先对训练图像应用随机样式转换，然后从这些经过样式转换的版本中选取部分区域作为补丁，并将原始图像中的选定区域随机替换成这些补丁。这种方法能够无缝兼容各种现有的风格转移算法，并且可以轻松集成到多种数据增强流程中。通过采用我们的策略，训练过程变得更加稳健，减少了过拟合的风险。比较实验表明，相对于先前的样式增强方法，我们的技术在性能上更胜一筹，并且收敛速度更快。
URL
https://arxiv.org/abs/2504.10563
