傅里叶基函数弥合增强差距：重新思考图像分类中的频率增强
Fourier-basis Functions to Bridge Augmentation Gap: Rethinking Frequency Augmentation in Image Classification
AI总结：
提出辅助傅里叶基增强（AFA），通过频域加性噪声补充视觉增强，提升模型对常见损坏、OOD泛化及扰动一致性的鲁棒性，且不影响标准性能。
摘要：
计算机视觉模型在部署到真实世界场景时，通常会因训练期间未考虑的输入意外变化而出现性能下降。数据增强通常用于解决此问题，旨在增加数据多样性并缩小训练与测试数据之间的分布差距。然而，常见的视觉增强可能无法保证计算机视觉模型的广泛鲁棒性。在本文中，我们提出辅助傅里叶基增强（AFA），这是一种针对频域增强的补充技术，填补了视觉增强留下的增强差距。我们在直接且高效的对抗性设置中展示了傅里叶基加性噪声增强的效用。我们的结果表明，AFA 有益于模型对常见损坏的鲁棒性、OOD 泛化以及模型在不断增强扰动下的性能一致性，而对模型的标准性能影响可忽略不计。它可以与其他增强技术无缝集成，以进一步提升性能。代码和模型可在以下网址找到：https://github.com/nis-research/afa-augment
英文摘要：
Computer vision models normally witness degraded performance when deployed in real-world scenarios, due to unexpected changes in inputs that were not accounted for during training. Data augmentation is commonly used to address this issue, as it aims to increase data variety and reduce the distribution gap between training and test data. However, common visual augmentations might not guarantee extensive robustness of computer vision models. In this paper, we propose Auxiliary Fourier-basis Augmentation (AFA), a complementary technique targeting augmentation in the frequency domain and filling the augmentation gap left by visual augmentations. We demonstrate the utility of augmentation via Fourier-basis additive noise in a straightforward and efficient adversarial setting. Our results show that AFA benefits the robustness of models against common corruptions, OOD generalization, and consistency of performance of models against increasing perturbations, with negligible deficit to the standard performance of models. It can be seamlessly integrated with other augmentation techniques to further boost performance. Code and models can be found at: https://github.com/nis-research/afa-augment
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
