Bo Han
Bo Han
Verified email at - Homepage
Cited by
Cited by
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
B Han, Q Yao, X Yu, G Niu, M Xu, W Hu, IW Tsang, M Sugiyama
NeurIPS 2018, 2018
How does Disagreement Help Generalization against Label Corruption?
X Yu, B Han, J Yao, G Niu, IW Tsang, M Sugiyama
ICML 2019, 2019
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger
J Zhang, X Xu, B Han, G Niu, L Cui, M Sugiyama, M Kankanhalli
ICML 2020, 2020
Are Anchor Points Really Indispensable in Label-Noise Learning?
X Xia, T Liu, N Wang, B Han, C Gong, G Niu, M Sugiyama
NeurIPS 2019, 2019
Masking: A New Perspective of Noisy Supervision
B Han, J Yao, G Niu, M Zhou, IW Tsang, Y Zhang, M Sugiyama
NeurIPS 2018, 2018
Geometry-aware Instance-reweighted Adversarial Training
J Zhang, J Zhu, G Niu, B Han, M Sugiyama, M Kankanhalli
ICLR 2021, 2021
Part-dependent Label Noise: Towards Instance-dependent Label Noise
X Xia, T Liu, B Han, N Wang, M Gong, H Liu, G Niu, D Tao, M Sugiyama
NeurIPS 2020, 2020
Reducing Estimation Error for Transition Matrix in Label-noise Learning
Y Yao, T Liu, B Han, M Gong, J Deng, G Niu, M Sugiyama
NeurIPS 2020, 2020
Robust Early-learning: Hindering the Memorization of Noisy Labels
X Xia, T Liu, B Han, C Gong, N Wang, Z Ge, Y Chang
ICLR 2021, 2021
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust
B Han, G Niu, X Yu, Q Yao, M Xu, IW Tsang, M Sugiyama
ICML 2020, 2020
Searching to Exploit Memorization Effect in Learning with Noisy Labels
Q Yao, H Yang, B Han, G Niu, JT Kwok
arXiv preprint arXiv:1911.02377, 2019
A Survey of Label-noise Representation Learning: Past, Present and Future
B Han, Q Yao, T Liu, G Niu, IW Tsang, JT Kwok, M Sugiyama
arXiv preprint arXiv:2011.04406, 2020
Confidence Scores Make Instance-dependent Label-noise Learning Possible
A Berthon, B Han, G Niu, T Liu, M Sugiyama
ICML 2021, 2021
Provably Consistent Partial-Label Learning
L Feng, J Lv, B Han, M Xu, G Niu, X Geng, B An, M Sugiyama
arXiv preprint arXiv:2007.08929, 2020
Learning with Multiple Complementary Labels
L Feng, T Kaneko, B Han, G Niu, B An, M Sugiyama
arXiv preprint arXiv:1912.12927, 2019
Understanding and Improving for Learning with Noisy Labels
Y Bai, E Yang, B Han, Y Yang, J Li, Y Mao, G Niu, T Liu
arXiv preprint arXiv:2106.15853, 2021
Provably End-to-end Label-Noise Learning without Anchor Points
X Li, T Liu, B Han, G Niu, M Sugiyama
arXiv preprint arXiv:2102.02400, 2021
Class2simi: A Noise Reduction Perspective on Learning with Noisy Labels
S Wu, X Xia, T Liu, B Han, M Gong, N Wang, H Liu, G Niu
arXiv preprint arXiv:2006.07831, 2020
Maximum Mean Discrepancy Test is Aware of Adversarial Attacks
R Gao, F Liu, J Zhang, B Han, T Liu, G Niu, M Sugiyama
ICML 2021, 2021
Towards Robust ResNet: A Small Step but A Giant Leap
J Zhang, B Han, L Wynter, KH Low, M Kankanhalli
IJCAI 2019, 2019
The system can't perform the operation now. Try again later.
Articles 1–20