On Feature Normalization and Data Augmentation
Table 11: The performance of ResNet-50 variants on ImageNet-A. The up-arrow represents the higher the better, the down-arrow represents the lower the better.
['Name', 'Acc↑', 'RMS↓', 'MAD↓', 'AURRA↑', 'Soft F1↑']
To estimate the robustness of the models trained with MoEx, we follow the procedure proposed by Hendrycks et al. In general MoEx performs fairly well across the board. The combination of MoEx and Cutmix leads to the best performance on most of the metrics.