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Recent Advances in Understanding Adversarial Robustness of Deep Neural Networks
[article]
2020
arXiv
pre-print
Adversarial examples are inevitable on the road of pervasive applications of deep neural networks (DNN). Imperceptible perturbations applied on natural samples can lead DNN-based classifiers to output wrong prediction with fair confidence score. It is increasingly important to obtain models with high robustness that are resistant to adversarial examples. In this paper, we survey recent advances in how to understand such intriguing property, i.e. adversarial robustness, from different
arXiv:2011.01539v1
fatcat:e3o47epftbc2rebpdx5yotzriy