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Implicit Euler Skip Connections: Enhancing Adversarial Robustness via Numerical Stability
2020
International Conference on Machine Learning
Deep neural networks have achieved great success in various areas, but recent works have found that neural networks are vulnerable to adversarial attacks, which leads to a hot topic nowadays. Although many approaches have been proposed to enhance the robustness of neural networks, few of them explored robust architectures for neural networks. On this account, we try to address such an issue from the perspective of dynamic system in this work. By viewing ResNet as an explicit Euler
dblp:conf/icml/LiHL20
fatcat:3ysie62t4rb7fjofbfnzqv3dzu