%0 Journal Article %1 tsipras2018robustness %A Tsipras, Dimitris %A Santurkar, Shibani %A Engstrom, Logan %A Turner, Alexander %A Madry, Aleksander %D 2018 %K adversarial readings robustness %T Robustness May Be at Odds with Accuracy %U http://arxiv.org/abs/1805.12152 %X We show that there exists an inherent tension between the goal of adversarial robustness and that of standard generalization. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. We demonstrate that this trade-off between the standard accuracy of a model and its robustness to adversarial perturbations provably exists even in a fairly simple and natural setting. These findings also corroborate a similar phenomenon observed in practice. Further, we argue that this phenomenon is a consequence of robust classifiers learning fundamentally different feature representations than standard classifiers. These differences, in particular, seem to result in unexpected benefits: the representations learned by robust models tend to align better with salient data characteristics and human perception.

@article{tsipras2018robustness, abstract = {We show that there exists an inherent tension between the goal of adversarial robustness and that of standard generalization. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. We demonstrate that this trade-off between the standard accuracy of a model and its robustness to adversarial perturbations provably exists even in a fairly simple and natural setting. These findings also corroborate a similar phenomenon observed in practice. Further, we argue that this phenomenon is a consequence of robust classifiers learning fundamentally different feature representations than standard classifiers. These differences, in particular, seem to result in unexpected benefits: the representations learned by robust models tend to align better with salient data characteristics and human perception.}, added-at = {2019-06-11T05:55:56.000+0200}, author = {Tsipras, Dimitris and Santurkar, Shibani and Engstrom, Logan and Turner, Alexander and Madry, Aleksander}, biburl = {https://www.bibsonomy.org/bibtex/213ed51434b74448cc9b2b15070109df0/kirk86}, description = {[1805.12152] Robustness May Be at Odds with Accuracy}, interhash = {4874c477d0bf0d5cec3a928ebedc6f88}, intrahash = {13ed51434b74448cc9b2b15070109df0}, keywords = {adversarial readings robustness}, note = {cite arxiv:1805.12152}, timestamp = {2019-09-26T15:45:39.000+0200}, title = {Robustness May Be at Odds with Accuracy}, url = {http://arxiv.org/abs/1805.12152}, year = 2018 }