| Learning with Instance-Dependent Label Noise: A Sample Sieve Approach H Cheng, Z Zhu, X Li, Y Gong, X Sun, Y Liu ICLR 2021 arXiv preprint arXiv:2010.02347, 2020 | 185 | 2020 |
| Learning with noisy labels revisited: A study using real-world human annotations J Wei, Z Zhu, H Cheng, T Liu, G Niu, Y Liu arXiv preprint arXiv:2110.12088, 2021 | 183 | 2021 |
| Asymmetric co-teaching for unsupervised cross-domain person re-identification F Yang, K Li, Z Zhong, Z Luo, X Sun, H Cheng, X Guo, F Huang, R Ji, S Li Proceedings of the AAAI conference on artificial intelligence 34 (07), 12597 …, 2020 | 172 | 2020 |
| Pruning Filter in Filter F Meng, H Cheng, K Li, H Luo, X Guo, G Lu, X Sun NeurlPS 2020, arXiv preprint arXiv:2009.14410, 2020 | 110 | 2020 |
| Removing the background by adding the background: Towards background robust self-supervised video representation learning J Wang, Y Gao, K Li, Y Lin, AJ Ma, H Cheng, P Peng, F Huang, R Ji, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 92 | 2021 |
| Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment Y Liu, Y Yao, JF Ton, X Zhang, RGH Cheng, Y Klochkov, MF Taufiq, H Li arXiv preprint arXiv:2308.05374, 2023 | 87 | 2023 |
| Do not disturb me: Person re-identification under the interference of other pedestrians S Zhao, C Gao, J Zhang, H Cheng, C Han, X Jiang, X Guo, WS Zheng, ... Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 52 | 2020 |
| Filter grafting for deep neural networks F Meng, H Cheng, K Li, Z Xu, R Ji, X Sun, G Lu CVPR 2020, 6599-6607, 2020 | 42 | 2020 |
| DisCo: Remedying Self-supervised Learning on Lightweight Models with Distilled Contrastive Learning Y Gao, JX Zhuang, S Lin, H Cheng, X Sun, K Li, C Shen European Conference on Computer Vision, 237-253, 2022 | 37 | 2022 |
| Evaluating capability of deep neural networks for image classification via information plane H Cheng, D Lian, S Gao, Y Geng ECCV 2018, 168-182, 2018 | 35 | 2018 |
| Mitigating memorization of noisy labels via regularization between representations H Cheng, Z Zhu, X Sun, Y Liu arXiv preprint arXiv:2110.09022, 2021 | 29* | 2021 |
| Identifiability of label noise transition matrix Y Liu, H Cheng, K Zhang International Conference on Machine Learning, 21475-21496, 2023 | 24 | 2023 |
| On the consistency training for open-set semi-supervised learning H Luo, H Cheng, Y Gao, K Li, M Zhang, F Meng, X Guo, F Huang, X Sun arXiv preprint arXiv:2101.08237 3 (6), 2021 | 17 | 2021 |
| Local to global learning: Gradually adding classes for training deep neural networks H Cheng, D Lian, B Deng, S Gao, T Tan, Y Geng Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 14 | 2019 |
| One for more: Selecting generalizable samples for generalizable ReID model E Zhang, X Jiang, H Cheng, A Wu, F Yu, K Li, X Guo, F Zheng, W Zheng, ... Proceedings of the AAAI Conference on Artificial Intelligence 35 (4), 3324-3332, 2021 | 10 | 2021 |
| RMNet: Equivalently Removing Residual Connection from Networks F Meng, H Cheng, J Zhuang, K Li, X Sun arXiv preprint arXiv:2111.00687, 2021 | 9 | 2021 |
| Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless Language Models Z Zhu, J Wang, H Cheng, Y Liu arXiv preprint arXiv:2311.11202, 2023 | 5 | 2023 |
| Filter Grafting for Deep Neural Networks: Reason, Method, and Cultivation H Cheng, F Meng, K Li, Y Gao, G Lu, X Sun, R Ji arXiv preprint arXiv:2004.12311, 2020 | 1 | 2020 |
| RobustTSF: Towards Theory and Design of Robust Time Series Forecasting with Anomalies H Cheng, Q Wen, Y Liu, L Sun arXiv preprint arXiv:2402.02032, 2024 | | 2024 |
Xing Sun 孙星Tencent在 eee.hku.hk 的电子邮件经过验证
