Learning Activation Functions for Adversarial Attack Resilience in CNNs

Author:

Salimi MaghsoodORCID,Loni MohammadORCID,Sirjani MarjanORCID

Publisher

Springer Nature Switzerland

Reference47 articles.

1. Bahramali, A., Nasr, M., Houmansadr, A., Goeckel, D., Towsley, D.: Robust adversarial attacks against DNN-based wireless communication systems. In: Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security, pp. 126–140 (2021)

2. Bingham, G., Macke, W., Miikkulainen, R.: Evolutionary optimization of deep learning activation functions. In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference, pp. 289–296 (2020)

3. Bingham, G., Miikkulainen, R.: Discovering parametric activation functions. arXiv preprint arXiv:2006.03179 (2020)

4. Bradshaw, J., Matthews, A.G.d.G., Ghahramani, Z.: Adversarial examples, uncertainty, and transfer testing robustness in gaussian process hybrid deep networks. arXiv preprint arXiv:1707.02476 (2017)

5. Burke, E.K., Bykov, Y., et al.: A late acceptance strategy in hill-climbing for exam timetabling problems. In: PATAT 2008 Conference, Montreal, Canada, pp. 1–7 (2008)

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