Towards robust neural networks via a global and monotonically decreasing robustness training strategy

Author:

Liang ZhenORCID,Wu Taoran,Liu WanweiORCID,Xue Bai,Yang Wenjing,Wang Ji,Pang Zhengbin

Publisher

Zhejiang University Press

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing

Reference45 articles.

1. Balunović M, Baader M, Singh G, et al., 2019. Certifying geometric robustness of neural networks. Proc 33rd Int Conf on Neural Information Processing Systems, Article 1372.

2. Bojarski M, Testa DD, Dworakowski D, et al., 2016. End to end learning for self-driving cars. https://arxiv.org/abs/1604.07316

3. Casadio M, Komendantskaya E, Daggitt ML, et al., 2022. Neural network robustness as a verification property: a principled case study. Proc 34th Int Conf on Computer Aided Verification, p.219–231. https://doi.org/10.1007/978-3-031-13185-1_11

4. Chen XL, He KM, 2021. Exploring simple Siamese representation learning. Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.15750–15758. https://doi.org/10.1109/CVPR46437.2021.01549

5. Cohen JM, Rosenfeld E, Kolter JZ, 2019. Certified adversarial robustness via randomized smoothing. Proc 36th Int Conf on Machine Learning, p.1310–1320.

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