论文标题
通过基于频率的增强来改善对分布数据的鲁棒性
Improving Robustness to Out-of-Distribution Data by Frequency-based Augmentation
论文作者
论文摘要
尽管卷积神经网络（CNN）在图像识别方面具有很高的准确性，但它们容易受到对抗性示例和分发数据的影响，并且已经指出了人类识别的差异。为了提高针对分布数据的鲁棒性，我们提出了一种基于频率的数据增强技术，该技术将频率组件用同一类的其他图像替换。当训练数据为CIFAR10并且分发数据的数据为SVHN时，经过培训的使用该方法训练的模型的接收器操作特征（AUROC）曲线从89.22 \％\％\％增加到98.15 \％，并且与另一种增强方法相结合时，将其进一步增加到98.59％。此外，我们在实验上证明了分布数据外数据的鲁棒模型使用图像的许多高频组件。
Although Convolutional Neural Networks (CNNs) have high accuracy in image recognition, they are vulnerable to adversarial examples and out-of-distribution data, and the difference from human recognition has been pointed out. In order to improve the robustness against out-of-distribution data, we present a frequency-based data augmentation technique that replaces the frequency components with other images of the same class. When the training data are CIFAR10 and the out-of-distribution data are SVHN, the Area Under Receiver Operating Characteristic (AUROC) curve of the model trained with the proposed method increases from 89.22\% to 98.15\%, and further increased to 98.59\% when combined with another data augmentation method. Furthermore, we experimentally demonstrate that the robust model for out-of-distribution data uses a lot of high-frequency components of the image.
