Follow
Jingfeng Zhang
Jingfeng Zhang
Postdoctoral researcher @RIKEN AIP
Verified email at riken.jp - Homepage
Title
Cited by
Cited by
Year
Geometry-aware Instance-reweighted Adversarial Training
J Zhang, J Zhu, G Niu, B Han, M Sugiyama, M Kankanhalli
International Conference on Learning Representations (ICLR 2021), 2021
1612021
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger
J Zhang, X Xu, B Han, G Niu, L Cui, M Sugiyama, M Kankanhalli
International Conference on Machine Learning (ICML 2020), 2020
1592020
Maximum Mean Discrepancy Test is Aware of Adversarial Attacks
R Gao, F Liu, J Zhang, B Han, T Liu, G Niu, M Sugiyama
International Conference on Machine Learning (ICML 2021), 2021
30*2021
Towards Robust Resnet: A Small Step but A Giant Leap
J Zhang, B Han, L Wynter, KH Low, M Kankanhalli
International Joint Conference on Artificial Intelligence (IJCAI 2019), 2019
302019
Hierarchically Fair Federated Learning
J Zhang, C Li, A Robles-Kelly, M Kankanhalli
Technical Report, 2020
242020
Learning Diverse-structured Networks for Adversarial Robustness
X Du, J Zhang, B Han, T Liu, Y Rong, G Niu, J Huang, M Sugiyama
International Conference on Machine Learning (ICML 2021), 2021
112021
CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection
H Yan, J Zhang, G Niu, J Feng, V Tan, M Sugiyama
International Conference on Machine Learning (ICML 2021), 2021
102021
Understanding the interaction of adversarial training with noisy labels
J Zhu, J Zhang, B Han, T Liu, G Niu, H Yang, M Kankanhalli, M Sugiyama
arXiv preprint arXiv:2102.03482, 2021
92021
Decision Boundary-aware Data Augmentation for Adversarial Training
C Chen, J Zhang, X Xu, L Lyu, C Chen, T Hu, G Chen
IEEE Transactions on Dependable and Secure Computing (TDSC 2022), 2022
3*2022
NoiLin: Improving Adversarial Training and Correcting Stereotype of Noisy Labels
J Zhang, X Xu, B Han, T Liu, L Cui, G Niu, M Sugiyama
Transactions on Machine Learning Research (TMLR 2022), 2022
2*2022
Reliable Adversarial Distillation with Unreliable Teachers
J Zhu, J Yao, B Han, J Zhang, T Liu, G Niu, J Zhou, J Xu, H Yang
International Conference on Learning Representations (ICLR 2022), 2022
22022
On the Effectiveness of Adversarial Training against Backdoor Attacks
Y Gao, D Wu, J Zhang, G Gan, ST Xia, G Niu, M Sugiyama
arXiv preprint arXiv:2202.10627, 2022
12022
WaveFuzz: A Clean-Label Poisoning Attack to Protect Your Voice
Y Ge, Q Wang, J Zhang, J Zhou, Y Zhang, C Shen
arXiv preprint arXiv:2203.13497, 2022
2022
Towards Adversarially Robust Deep Image Denoising
H Yan, J Zhang, J Feng, M Sugiyama, VYF Tan
International Joint Conference on Artificial Intelligence (IJCAI 2022), 2022
2022
Adversarial Attack and Defense for Non-Parametric Two-Sample Tests
X Xu, J Zhang, F Liu, M Sugiyama, M Kankanhalli
International Conference on Machine Learning (ICML 2022), 2022
2022
Bilateral Dependency Optimization: Defending Against Model-inversion Attacks
X Peng, F Liu, J Zhang, L Lan, J Ye, T Liu, B Han
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining …, 2022
2022
The system can't perform the operation now. Try again later.
Articles 1–16