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Tianyu Pang
Tianyu Pang
Research Scientist, Sea AI Lab
Verified email at sea.com - 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
14762018
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
5972018
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
3232019
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
2612019
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
2402018
Towards robust detection of adversarial examples
T Pang, C Du, Y Dong, J Zhu
Annual Conference on Neural Information Processing Systems (NeurIPS 2018), 2018
159*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
1522019
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
1342020
Bag of tricks for adversarial training
T Pang, X Yang, Y Dong, H Su, J Zhu
International Conference on Learning Representations (ICLR 2021), 2021
1062021
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
932020
Mixup inference: Better exploiting mixup to defend adversarial attacks
T Pang, K Xu, J Zhu
International Conference on Learning Representations (ICLR 2020), 2020
772020
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
582020
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
412020
Max-mahalanobis linear discriminant analysis networks
T Pang, C Du, J Zhu
International Conference on Machine Learning (ICML 2018), 2018
372018
Towards privacy protection by generating adversarial identity masks
X Yang, Y Dong, T Pang, J Zhu, H Su
International Conference on Computer Vision (ICCV 2021), 2021
18*2021
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
International Conference on Computer Vision (ICCV 2021), 2021
172021
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
122020
Experimental realization of open magnetic shielding
C Gu, S Chen, T Pang, TM Qu
Applied Physics Letters 110 (19), 193505, 2017
122017
Exploring Memorization in Adversarial Training
Y Dong, K Xu, X Yang, T Pang, Z Deng, H Su, J Zhu
International Conference on Learning Representations (ICLR 2022), 2022
112022
Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart
T Pang, H Zhang, D He, Y Dong, H Su, W Chen, J Zhu, TY Liu
IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2022), 2022
8*2022
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