Tianyu Pang
Tianyu Pang
Ph.D. Candidate in Computer Science, Tsinghua University
Verified email at mails.tsinghua.edu.cn - Homepage
Title
Cited by
Cited by
Year
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
7262018
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
3352018
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
1542018
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
1082019
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
1022019
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
592019
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
322020
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
252020
Max-mahalanobis linear discriminant analysis networks
T Pang, C Du, J Zhu
International Conference on Machine Learning (ICML 2018), 2018
242018
Experimental realization of open magnetic shielding
C Gu, S Chen, T Pang, TM Qu
Applied Physics Letters 110 (19), 193505, 2017
102017
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
92020
Bag of tricks for adversarial training
T Pang, X Yang, Y Dong, H Su, J Zhu
International Conference on Learning Representations (ICLR 2021), 2021
82021
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
42020
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
22020
Towards privacy protection by generating adversarial identity masks
X Yang, Y Dong, T Pang, J Zhu, H Su
arXiv preprint arXiv:2003.06814, 2020
22020
Geometric Universality of Adversarial Examples in Deep Learning
H Zou, H Su, T Pang, J Zhu
Geometry in Machine Learning ICML Workshop (GIML), 2018
22018
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
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Articles 1–19