Learnable Boundary Guided Adversarial Training-Reference-Cited by-同舟云学术

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Learnable Boundary Guided Adversarial Training

Published:2021-10 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE/CVF International Conference on Computer Vision (ICCV)
language:
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Author:

Cui Jiequan¹,Liu Shu²,Wang Liwei¹,Jia Jiaya¹

Affiliation:

1. The Chinese University of Hong Kong

2. SmartMore

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9709627/9709628/09710526.pdf?arnumber=9710526

Reference58 articles.

1. Intriguing properties of neural networks;szegedy;ICLRE,2014

2. Improving adversarial robustness through progressive hardening;sitawarin,2020

3. Overfitting in adversarially robust deep learning;rice;ICML,2020

4. Adversarial robustness through local linearization;qin;NeurIPS,2019

5. Relational Knowledge Distillation

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1. Exploring Robust Features for Improving Adversarial Robustness;IEEE Transactions on Cybernetics;2024-09

2. Tightening the Approximation Error of Adversarial Risk with Auto Loss Function Search;Proceedings of the Genetic and Evolutionary Computation Conference Companion;2024-07-14

3. Data filtering for efficient adversarial training;Pattern Recognition;2024-07

4. Attention-based investigation and solution to the trade-off issue of adversarial training;Neural Networks;2024-06

5. RDAT: an efficient regularized decoupled adversarial training mechanism;International Journal of Multimedia Information Retrieval;2024-05-07

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