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Tianyu Pang
Tianyu Pang
Senior Research Scientist, Sea AI Lab
Zweryfikowany adres z sea.com - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
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
27922018
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
9202018
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
7882019
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
4342019
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
356*2018
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
2772020
Bag of tricks for adversarial training
T Pang, X Yang, Y Dong, H Su, J Zhu
International Conference on Learning Representations (ICLR 2021), 2021
2652021
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
2622019
Towards robust detection of adversarial examples
T Pang, C Du, Y Dong, J Zhu
Annual Conference on Neural Information Processing Systems (NeurIPS 2018), 2018
246*2018
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
1752020
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
1462020
Mixup inference: Better exploiting mixup to defend adversarial attacks
T Pang, K Xu, J Zhu
International Conference on Learning Representations (ICLR 2020), 2020
1282020
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
1072020
Better diffusion models further improve adversarial training
Z Wang, T Pang, C Du, M Lin, W Liu, S Yan
International Conference on Machine Learning (ICML 2023), 2023
982023
Robustness and Accuracy Could Be Reconcilable by (Proper) Definition
T Pang, M Lin, X Yang, J Zhu, S Yan
International Conference on Machine Learning (ICML 2022), 2022
942022
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
912021
Towards face encryption by generating adversarial identity masks
X Yang, Y Dong, T Pang, J Zhu, H Su
International Conference on Computer Vision (ICCV 2021), 2021
702021
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
642022
Max-mahalanobis linear discriminant analysis networks
T Pang, C Du, J Zhu
International Conference on Machine Learning (ICML 2018), 2018
532018
A recipe for watermarking diffusion models
Y Zhao, T Pang, C Du, X Yang, NM Cheung, M Lin
arXiv preprint arXiv:2303.10137, 2023
502023
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