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Jacobian Adversarially Regularized Networks for Robustness
[article]
2020
arXiv
pre-print
Adversarial examples are crafted with imperceptible perturbations with the intent to fool neural networks. Against such attacks, adversarial training and its variants stand as the strongest defense to date. Previous studies have pointed out that robust models that have undergone adversarial training tend to produce more salient and interpretable Jacobian matrices than their non-robust counterparts. A natural question is whether a model trained with an objective to produce salient Jacobian can
arXiv:1912.10185v2
fatcat:nbucatybifaoza6cdoqz7exk4i