Abstract
This paper proposes a training data augmentation pipeline that combines synthetic image data with neural style transfer in order to address the vulnerability of deep vision models to common corruptions. We show that although applying style transfer on synthetic images degrades their quality with respect to the common FID metric, these images are surprisingly beneficial for model training. We conduct a systematic empirical analysis of the effects of both augmentations and their key hyperparameters on the performance of image classifiers. Our results demonstrate that stylization and synthetic data complement each other well and can be combined with popular rule-based data augmentation techniques such as TrivialAugment, while not working with others. Our method achieves state-of-the-art corruption robustness on several small-scale image classification benchmarks, reaching 93.54%, 74.9% and 50.86% robust accuracy on CIFAR-10-C, CIFAR-100-C and TinyImageNet-C, respectively
Abstract (translated)
本文提出了一种训练数据增强管道，该管道结合了合成图像数据与神经风格转换技术，旨在解决深度视觉模型在面对常见干扰时的脆弱性问题。我们发现，尽管在合成图像上应用风格迁移会降低它们的质量（根据常见的FID度量标准），但这些图像对模型训练来说却意外地非常有益。 本文系统地分析了这两种增强方法及其关键超参数对图像分类器性能的影响。研究结果表明，风格化与合成数据可以很好地相互补充，并且能够与流行的基于规则的数据增强技术（如TrivialAugment）结合使用，但不能与其他某些方法一起使用。 我们的方法在几个小规模的图像分类基准测试中达到了最先进的抗干扰性，在CIFAR-10-C、CIFAR-100-C和TinyImageNet-C上的鲁棒准确率分别达到93.54%、74.9%和50.86%。
URL
https://arxiv.org/abs/2512.15675
