Artykuły udostępnione publicznie: - Bo HanWięcej informacji
Wstrzymanych: 1
Class-Wise Denoising for Robust Learning under Label Noise
C Gong, Y Ding, B Han, G Niu, J Yang, JJ You, D Tao, M Sugiyama
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022
Upoważnienia: National Natural Science Foundation of China
Przesłany przez: C Gong
Dostępne w jakimś miejscu: 111
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
Upoważnienia: Research Foundation (Flanders), National Natural Science Foundation of China
How does Disagreement Help Generalization against Label Corruption?
X Yu, B Han, J Yao, G Niu, IW Tsang, M Sugiyama
ICML 2019, 2019
Upoważnienia: Research Foundation (Flanders)
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
Upoważnienia: National Natural Science Foundation of China, Research Grants Council, Hong …
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
Upoważnienia: Australian Research Council, National Natural Science Foundation of China
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
Upoważnienia: Australian Research Council, National Natural Science Foundation of China
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
Upoważnienia: Australian Research Council, National Natural Science Foundation of China
Masking: A New Perspective of Noisy Supervision
B Han, J Yao, G Niu, M Zhou, IW Tsang, Y Zhang, M Sugiyama
NeurIPS 2018, 2018
Upoważnienia: US National Science Foundation, Research Foundation (Flanders), National …
Learning Causally Invariant Representations for Out-of-distribution Generalization on Graphs
Y Chen, Y Zhang, Y Bian, H Yang, K Ma, B Xie, T Liu, B Han, J Cheng
arXiv preprint arXiv:2202.05441, 2022
Upoważnienia: Australian Research Council, National Natural Science Foundation of China
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
Upoważnienia: National Natural Science Foundation of China, National Research Foundation …
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
Upoważnienia: Australian Research Council, Research Foundation (Flanders), Research Grants …
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
Upoważnienia: Research Grants Council, Hong Kong
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
Upoważnienia: Australian Research Council, National Natural Science Foundation of China
Is Out-of-distribution Detection Learnable?
Z Fang, S Li, J Lu, J Dong, B Han, F Liu
NeurIPS 2022, 2022
Upoważnienia: US Department of Defense, Australian Research Council, National Natural …
Confidence Scores Make Instance-dependent Label-noise Learning Possible
A Berthon, B Han, G Niu, T Liu, M Sugiyama
ICML 2021, 2021
Upoważnienia: Australian Research Council, National Natural Science Foundation of China
Learning with Multiple Complementary Labels
L Feng, T Kaneko, B Han, G Niu, B An, M Sugiyama
arXiv preprint arXiv:1912.12927, 2019
Upoważnienia: Research Grants Council, Hong Kong, National Research Foundation, Singapore
Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning
Z Tang, Y Zhang, S Shi, X He, B Han, X Chu
ICML 2022, 2022
Upoważnienia: National Natural Science Foundation of China
Moderate coreset: A universal method of data selection for real-world data-efficient deep learning
X Xia, J Liu, J Yu, X Shen, B Han, T Liu
The Eleventh International Conference on Learning Representations, 2022
Upoważnienia: Australian Research Council, National Natural Science Foundation of China
Instance-dependent Label-noise Learning with Manifold-regularized Transition Matrix Estimation
D Cheng, T Liu, Y Ning, N Wang, B Han, G Niu, X Gao, M Sugiyama
CVPR 2022, 2022
Upoważnienia: Australian Research Council, National Natural Science Foundation of China
Instance-dependent Label-noise Learning under Structural Causal Models
Y Yao, T Liu, M Gong, B Han, G Niu, K Zhang
NeurIPS 2021, 2021
Upoważnienia: US National Science Foundation, US Department of Defense, US National …
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