280 citations · 1.2k across the 24 of their papers we have counts for
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cs.CV2021★ 3 cited
Defending Against Image Corruptions Through Adversarial Augmentations
Dan A. Calian, Florian Stimberg, Olivia Wiles +4
Modern neural networks excel at image classification, yet they remain vulnerable to common image corruptions such as blur, speckle noise or fog. Recent methods that focus on this p…
cs.CV2021★ 104 cited
Fixing Data Augmentation to Improve Adversarial Robustness
Sylvestre-Alvise Rebuffi, Sven Gowal, Dan A. Calian +3
Adversarial training suffers from robust overfitting, a phenomenon where the robust test accuracy starts to decrease during training. In this paper, we focus on both heuristics-dri…