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Spatial Frequency Sensitivity Regularization for Robustness
The ability to generalize to out-of-distribution data is a major challenge for modern deep neural networks.
ARMOURED: Adversarially Robust MOdels using Unlabeled data by REgularizing Diversity
In this paper, we propose ARMOURED, an adversarially robust training method based on semi-supervised learning that consists of two components.
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