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Deep neural networks are incredibly vulnerable to crafted, human-imperceptible adversarial perturbations.
Intriguing properties of neural networks
Szegedy, C.; Zaremba, W.; Sutskever, I.; Bruna, J.; Erhan, D.; Goodfellow, I.; and Fergus, R. 2013 · 2013
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, I. J.; Shlens, J.; and Szegedy, C. 2014 · 2014
Earlier work this paper cites.
Spectral Representations for Convolutional Neural Networks
Rippel, O.; Snoek, J.; and Adams, R. P. 2015 · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Earlier work this paper cites.
Towards evaluating the robustness of neural networks
Carlini, N.; and Wagner, D. 2017 · 2017
Earlier work this paper cites.
Towards deep learning models resistant to adversarial attacks
Madry, A.; Makelov, A.; Schmidt, L.; Tsipras, D.; and Vladu, A. 2017 · 2017
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, A.; Carlini, N.; and Wagner, D. 2018 · 2018
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Averaging weights leads to wider optima and better generalization
Izmailov, P.; Podoprikhin, D.; Garipov, T.; Vetrov, D.; and Wilson, A. G. 2018 · 2018
Cited alongside, same era.
Mr-net: Exploiting mutual relation for visual relationship detection
Bin, Y.; Yang, Y.; Tao, C.; Huang, Z.; Li, J.; and Shen, H. T. 2019 · 2019
Cited alongside, same era.
Improving adversarial robustness requires revisiting misclassified examples
Wang, Y.; Zou, D.; Yi, J.; Bailey, J.; Ma, X.; and Gu, Q. 2019 · 2019
Cited alongside, same era.
Theoretically principled trade-off between robustness and accuracy
Zhang, H.; Yu, Y.; Jiao, J.; Xing, E.; El Ghaoui, L.; and Jordan, M. 2019 · 2019
Cited alongside, same era.
Robust overfitting may be mitigated by properly learned smoothening
Chen, T.; Zhang, Z.; Liu, S.; Chang, S.; and Wang, Z. 2020 · 2020
Cited alongside, same era.
Overfitting in adversarially robust deep learning
Rice, L.; Wong, E.; and Kolter, Z. 2020 · 2020
Later among the works it cites.
Dynamic and static context-aware lstm for multi-agent motion prediction
Tao, C.; Jiang, Q.; Duan, L.; and Luo, P. 2020 · 2020
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High-frequency component helps explain the generalization of convolutional neural networks
Wang, H.; Wu, X.; Huang, Z.; and Xing, E. P. 2020 · 2020
Later among the works it cites.
Adversarial weight perturbation helps robust generalization
Wu, D.; Xia, S.-T.; and Wang, Y. 2020 · 2020
Later among the works it cites.
Litegt: Efficient and lightweight graph transformers
Chen, C.; Tao, C.; and Wong, N. 2021 · 2021
Later among the works it cites.
FAT: Frequency-Aware Transformation for Bridging Full-Precision and Low-Precision Deep Representations
Tao, C.; Lin, R.; Chen, Q.; Zhang, Z.; Luo, P.; and Wong, N. 2022 · 2022
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Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Croce, F.; and Hein, M. 2020 · 2020
Cited alongside, same era.
Closest in time.