| Few-shot learning via embedding adaptation with set-to-set functions HJ Ye, H Hu, DC Zhan, F Sha Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 811* | 2020 |
| Learning placeholders for open-set recognition DW Zhou, HJ Ye, DC Zhan Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 195 | 2021 |
| Foster: Feature boosting and compression for class-incremental learning FY Wang, DW Zhou, HJ Ye, DC Zhan European conference on computer vision, 398-414, 2022 | 164 | 2022 |
| Forward compatible few-shot class-incremental learning DW Zhou, FY Wang, HJ Ye, L Ma, S Pu, DC Zhan Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 159 | 2022 |
| Deep class-incremental learning: A survey DW Zhou, QW Wang, ZH Qi, HJ Ye, DC Zhan, Z Liu arXiv preprint arXiv:2302.03648, 2023 | 105 | 2023 |
| Identifying and compensating for feature deviation in imbalanced deep learning HJ Ye, HY Chen, DC Zhan, WL Chao arXiv preprint arXiv:2001.01385, 2020 | 87 | 2020 |
| A model or 603 exemplars: Towards memory-efficient class-incremental learning DW Zhou, QW Wang, HJ Ye, DC Zhan arXiv preprint arXiv:2205.13218, 2022 | 74 | 2022 |
| Learning adaptive classifiers synthesis for generalized few-shot learning HJ Ye, H Hu, DC Zhan International Journal of Computer Vision 129 (6), 1930-1953, 2021 | 73 | 2021 |
| Few-shot class-incremental learning by sampling multi-phase tasks DW Zhou, HJ Ye, L Ma, D Xie, S Pu, DC Zhan IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 | 70 | 2022 |
| Decaug: Out-of-distribution generalization via decomposed feature representation and semantic augmentation H Bai, R Sun, L Hong, F Zhou, N Ye, HJ Ye, SHG Chan, Z Li Proceedings of the AAAI Conference on Artificial Intelligence 35 (8), 6705-6713, 2021 | 58 | 2021 |
| Co-transport for class-incremental learning DW Zhou, HJ Ye, DC Zhan Proceedings of the 29th ACM International Conference on Multimedia, 1645-1654, 2021 | 49 | 2021 |
| What makes objects similar: A unified multi-metric learning approach HJ Ye, DC Zhan, XM Si, Y Jiang, ZH Zhou Advances in neural information processing systems 29, 2016 | 46 | 2016 |
| Pycil: A python toolbox for class-incremental learning DW Zhou, FY Wang, HJ Ye, DC Zhan Science China Information Sciences 66 (9), 197101, 2023 | 45 | 2023 |
| Fast generalization rates for distance metric learning: Improved theoretical analysis for smooth strongly convex distance metric learning HJ Ye, DC Zhan, Y Jiang Machine Learning 108, 267-295, 2019 | 44 | 2019 |
| Distilling cross-task knowledge via relationship matching HJ Ye, S Lu, DC Zhan Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 41 | 2020 |
| Few-shot learning with a strong teacher HJ Ye, L Ming, DC Zhan, WL Chao IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 | 40 | 2022 |
| How to train your MAML to excel in few-shot classification HJ Ye, WL Chao arXiv preprint arXiv:2106.16245, 2021 | 40 | 2021 |
| Few-shot learning with adaptively initialized task optimizer: a practical meta-learning approach HJ Ye, XR Sheng, DC Zhan Machine Learning 109 (3), 643-664, 2020 | 37 | 2020 |
| The capacity and robustness trade-off: Revisiting the channel independent strategy for multivariate time series forecasting L Han, HJ Ye, DC Zhan IEEE Transactions on Knowledge and Data Engineering, 2024 | 35 | 2024 |
| Rank consistency based multi-view learning: A privacy-preserving approach HJ Ye, DC Zhan, Y Miao, Y Jiang, ZH Zhou Proceedings of the 24th ACM international on conference on information and …, 2015 | 33 | 2015 |
De-Chuan ZhanNanjing University, ChinaDirección de correo verificada de nju.edu.cn
