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 International Conference on Machine Learning, 11285-11295, 2021 | 74 | 2021 |
Bridging the gap between few-shot and many-shot learning via distribution calibration S Yang, S Wu, T Liu, M Xu IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (12), 9830 …, 2021 | 33 | 2021 |
Fair classification with instance-dependent label noise S Wu, M Gong, B Han, Y Liu, T Liu Conference on Causal Learning and Reasoning, 927-943, 2022 | 26 | 2022 |
Multi-class classification from noisy-similarity-labeled data S Wu, X Xia, T Liu, B Han, M Gong, N Wang, H Liu, G Niu arXiv preprint arXiv:2002.06508, 2020 | 13 | 2020 |
Learning from noisy pairwise similarity and unlabeled data S Wu, T Liu, B Han, J Yu, G Niu, M Sugiyama Journal of Machine Learning Research 23 (307), 1-34, 2022 | 4 | 2022 |
A parametrical model for instance-dependent label noise S Yang, S Wu, E Yang, B Han, Y Liu, M Xu, G Niu, T Liu IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 | 2 | 2023 |
A Time-Consistency Curriculum for Learning from Instance-Dependent Noisy Labels S Wu, T Zhou, Y Du, J Yu, B Han, T Liu IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024 | 1 | 2024 |
Unleashing the Potential of Adaptation Models via Go-getting Domain Labels X Jin, T He, X Shen, S Wu, T Liu, J Ye, X Wang, J Huang, Z Chen, XS Hua European Conference on Computer Vision, 308-325, 2022 | 1 | 2022 |
Unleash the potential of adaptation models via dynamic domain labels X Jin, T He, X Shen, S Wu, T Liu, X Wang, J Huang, Z Chen, XS Hua | 1 | 2022 |
Learning with Noisy Labels Incorporating Fairness and Privacy Concerns S Wu | | 2023 |