Raphael Gontijo Lopes, Dong Yin, Ben Poole, Justin Gilmer, Ekin D. Cubuk. Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation. CoRR abs/1906.02611 (2019).
Lopes et al. propose patch-based Gaussian data augmentation to improve accuracy and robustness against common corruptions. Their approach is intended to be an interpolation between Gaussian noise data augmentation and CutOut. During training, random patches on images are selected and random Gaussian noise is added to these patches. With increasing noise level (i.e., its standard deviation) this results in CutOut; with increasing patch size, this results in regular Gaussian noise data augmentation. On ImageNet-C and Cifar-C, the authors show that this approach improves robustness against common corruptions while also improving accuracy slightly.
Lopes et al. propose patch-based Gaussian data augmentation to improve accuracy and robustness against common corruptions. Their approach is intended to be an interpolation between Gaussian noise data augmentation and CutOut. During training, random patches on images are selected and random Gaussian noise is added to these patches. With increasing noise level (i.e., its standard deviation) this results in CutOut; with increasing patch size, this results in regular Gaussian noise data augmentation. On ImageNet-C and Cifar-C, the authors show that this approach improves robustness against common corruptions while also improving accuracy slightly.