Variance-Reduced Randomized Kaczmarz Algorithm in Xfel Single-Particle Imaging Phase Retrieval

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 frequency-based data augmentation 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 curve (AUROC) 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