| 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 | 155 | 2019 |
| 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 (spotlight), 2020 | 91 | 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 | 58 | 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 ICML 2021, 2021 | 38* | 2021 |
| Extended T: Learning with mixed closed-set and open-set noisy labels X Xia, B Han, N Wang, J Deng, J Li, Y Mao, T Liu IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 | 15 | 2022 |
| Learning lightweight super-resolution networks with weight pruning X Jiang, N Wang, J Xin, X Xia, X Yang, X Gao Neural Networks 144, 21-32, 2021 | 9 | 2021 |
| Objects in semantic topology S Yang, P Sun, Y Jiang, X Xia, R Zhang, Z Yuan, C Wang, P Luo, M Xu ICLR 2022, 2022 | 7 | 2022 |
| Sample selection with uncertainty of losses for learning with noisy labels X Xia, T Liu, B Han, M Gong, J Yu, G Niu, M Sugiyama ICLR 2022, 2022 | 6 | 2022 |
| Selective-supervised contrastive learning with noisy labels S Li, X Xia, S Ge, T Liu CVPR 2022, 2022 | 4 | 2022 |
| Instance correction for learning with open-set noisy labels X Xia, T Liu, B Han, M Gong, J Yu, G Niu, M Sugiyama arxiv preprint arXiv:2106.00455, 2021 | 3 | 2021 |
| LR-SVM+: Learning using privileged information with noisy labels Z Wu, X Xia, R Wang, J Li, J Yu, Y Mao, T Liu IEEE Transactions on Multimedia 24 (1), 1080-1092, 2021 | 1 | 2021 |
| Harnessing out-of-distribution examples via augmenting content and style Z Huang, X Xia, L Shen, B Han, M Gong, C Gong, T Liu arxiv preprint arXiv:2207.03162, 2022 | | 2022 |
| Pluralistic image completion with probabilistic mixture-of-experts X Xia, W Yang, J Ren, Y Li, Y Zhan, B Han, T Liu arxiv preprint arXiv:2205.09086, 2022 | | 2022 |
| Kernel mean estimation by marginalized corrupted distributions X Xia, S Shan, M Gong, N Wang, F Gao, H Wei, T Liu SIGKDD 2022, 2022 | | 2022 |
Tongliang LiuDirector, Sydney AI Centre, University of SydneyVerified email at sydney.edu.au
