| Boosting adversarial attacks with momentum Y Dong, F Liao, T Pang, H Su, J Zhu, X Hu, J Li Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 1403 | 2018 |
| Defense against adversarial attacks using high-level representation guided denoiser F Liao, M Liang, Y Dong, T Pang, X Hu, J Zhu Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 572 | 2018 |
| Evading defenses to transferable adversarial examples by translation-invariant attacks Y Dong, T Pang, H Su, J Zhu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 300 | 2019 |
| Technical report on the cleverhans v2. 1.0 adversarial examples library N Papernot, F Faghri, N Carlini, I Goodfellow, R Feinman, A Kurakin, ... arXiv preprint arXiv:1610.00768, 2016 | 245 | 2016 |
| Adversarial attacks and defences competition A Kurakin, I Goodfellow, S Bengio, Y Dong, F Liao, M Liang, T Pang, ... The NIPS'17 Competition: Building Intelligent Systems, 195-231, 2018 | 219 | 2018 |
| Efficient decision-based black-box adversarial attacks on face recognition Y Dong, H Su, B Wu, Z Li, W Liu, T Zhang, J Zhu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 212 | 2019 |
| Towards robust detection of adversarial examples T Pang, C Du, Y Dong, J Zhu NeurIPS 2018, 2017 | 155* | 2017 |
| Improving black-box adversarial attacks with a transfer-based prior S Cheng, Y Dong, T Pang, H Su, J Zhu NeurIPS 2019, 2019 | 144 | 2019 |
| Benchmarking adversarial robustness on image classification Y Dong, QA Fu, X Yang, T Pang, H Su, Z Xiao, J Zhu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 125 | 2020 |
| Improving interpretability of deep neural networks with semantic information Y Dong, H Su, J Zhu, B Zhang Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 100 | 2017 |
| Bag of tricks for adversarial training T Pang, X Yang, Y Dong, H Su, J Zhu ICLR 2021, 2020 | 98 | 2020 |
| Towards interpretable deep neural networks by leveraging adversarial examples Y Dong, H Su, J Zhu, F Bao AAAI 2019 Workshop on Network Interpretability for Deep Learning, 2017 | 93 | 2017 |
| Rethinking softmax cross-entropy loss for adversarial robustness T Pang, K Xu, Y Dong, C Du, N Chen, J Zhu ICLR 2020, 2019 | 87 | 2019 |
| Forecast the Plausible Paths in Crowd Scenes. H Su, J Zhu, Y Dong, B Zhang IJCAI 1, 2, 2017 | 65 | 2017 |
| Learning visual knowledge memory networks for visual question answering Z Su, C Zhu, Y Dong, D Cai, Y Chen, J Li Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 61 | 2018 |
| Boosting adversarial training with hypersphere embedding T Pang, X Yang, Y Dong, K Xu, J Zhu, H Su NeurIPS 2020, 2020 | 57 | 2020 |
| Batch virtual adversarial training for graph convolutional networks Z Deng, Y Dong, J Zhu ICML 2019 Workshop on Learning and Reasoning with Graph-Structured …, 2019 | 53 | 2019 |
| Crowd Scene Understanding with Coherent Recurrent Neural Networks. H Su, Y Dong, J Zhu, H Ling, B Zhang IJCAI 1, 2, 2016 | 52 | 2016 |
| Learning accurate low-bit deep neural networks with stochastic quantization Y Dong, R Ni, J Li, Y Chen, J Zhu, H Su British Machine Vision Conference, 2017 | 44 | 2017 |
| Adversarial Distributional Training for Robust Deep Learning Y Dong, Z Deng, T Pang, H Su, J Zhu Advances in Neural Information Processing Systems, 2020 | 40 | 2020 |
Jun ZhuBosch AI Professor, Department of Computer Science, Tsinghua University在 mail.tsinghua.edu.cn 的电子邮件经过验证
