| Boosting adversarial attacks with momentum Y Dong, F Liao, T Pang, H Su, J Zhu, X Hu, J Li IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), 2018 | 726 | 2018 |
| Defense against adversarial attacks using high-level representation guided denoiser F Liao, M Liang, Y Dong, T Pang, X Hu, J Zhu IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), 2018 | 335 | 2018 |
| 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 | 154 | 2018 |
| Evading defenses to transferable adversarial examples by translation-invariant attacks Y Dong, T Pang, H Su, J Zhu IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2019), 2019 | 108 | 2019 |
| Improving adversarial robustness via promoting ensemble diversity T Pang, K Xu, C Du, N Chen, J Zhu International Conference on Machine Learning (ICML 2019), 2019 | 102 | 2019 |
| Towards robust detection of adversarial examples T Pang, C Du, Y Dong, J Zhu Annual Conference on Neural Information Processing Systems (NeurIPS 2018), 2018 | 96* | 2018 |
| Improving black-box adversarial attacks with a transfer-based prior S Cheng, Y Dong, T Pang, H Su, J Zhu Annual Conference on Neural Information Processing Systems (NeurIPS 2019), 2019 | 59 | 2019 |
| Rethinking softmax cross-entropy loss for adversarial robustness T Pang, K Xu, Y Dong, C Du, N Chen, J Zhu International Conference on Learning Representations (ICLR 2020), 2020 | 32 | 2020 |
| Benchmarking adversarial robustness on image classification Y Dong, QA Fu, X Yang, T Pang, H Su, Z Xiao, J Zhu IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2020), 2020 | 28* | 2020 |
| Mixup inference: Better exploiting mixup to defend adversarial attacks T Pang, K Xu, J Zhu International Conference on Learning Representations (ICLR 2020), 2020 | 25 | 2020 |
| Max-mahalanobis linear discriminant analysis networks T Pang, C Du, J Zhu International Conference on Machine Learning (ICML 2018), 2018 | 24 | 2018 |
| Experimental realization of open magnetic shielding C Gu, S Chen, T Pang, TM Qu Applied Physics Letters 110 (19), 193505, 2017 | 10 | 2017 |
| Boosting adversarial training with hypersphere embedding T Pang, X Yang, Y Dong, K Xu, H Su, J Zhu Annual Conference on Neural Information Processing Systems (NeurIPS 2020), 2020 | 9 | 2020 |
| Bag of tricks for adversarial training T Pang, X Yang, Y Dong, H Su, J Zhu International Conference on Learning Representations (ICLR 2021), 2021 | 8 | 2021 |
| Adversarial Distributional Training for Robust Deep Learning Z Deng, Y Dong, T Pang, H Su, J Zhu Annual Conference on Neural Information Processing Systems (NeurIPS 2020), 2020 | 4 | 2020 |
| Efficient learning of generative models via finite-difference score matching T Pang, K Xu, C Li, Y Song, S Ermon, J Zhu Annual Conference on Neural Information Processing Systems (NeurIPS 2020), 2020 | 2 | 2020 |
| Towards privacy protection by generating adversarial identity masks X Yang, Y Dong, T Pang, J Zhu, H Su arXiv preprint arXiv:2003.06814, 2020 | 2 | 2020 |
| Geometric Universality of Adversarial Examples in Deep Learning H Zou, H Su, T Pang, J Zhu Geometry in Machine Learning ICML Workshop (GIML), 2018 | 2 | 2018 |
| Black-box Detection of Backdoor Attacks with Limited Information and Data Y Dong, X Yang, Z Deng, T Pang, Z Xiao, H Su, J Zhu arXiv preprint arXiv:2103.13127, 2021 | | 2021 |
Jun ZhuProfessor of Computer Science, Tsinghua UniversityVerified email at mail.tsinghua.edu.cn
