| 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 | 820 | 2020 |
| Supervised nonlinear dimensionality reduction for visualization and classification X Geng, DC Zhan, ZH Zhou IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 35 …, 2005 | 593 | 2005 |
| 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 | 262 | 2022 |
| Learning placeholders for open-set recognition DW Zhou, HJ Ye, DC Zhan Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 244 | 2021 |
| Semi-supervised learning with very few labeled training examples ZH Zhou, DC Zhan, Q Yang AAAI, 675-680, 2007 | 221 | 2007 |
| 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 | 207 | 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 | 200 | 2023 |
| 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 | 134 | 2022 |
| 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 45 (11 …, 2022 | 98 | 2022 |
| Identifying and compensating for feature deviation in imbalanced deep learning HJ Ye, HY Chen, DC Zhan, WL Chao arXiv preprint arXiv:2001.01385, 2020 | 97 | 2020 |
| Adaptive deep models for incremental learning: Considering capacity scalability and sustainability Y Yang, DW Zhou, DC Zhan, H Xiong, Y Jiang Proceedings of the 25th ACM SIGKDD international conference on knowledge …, 2019 | 97 | 2019 |
| Fedrs: Federated learning with restricted softmax for label distribution non-iid data XC Li, DC Zhan Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data …, 2021 | 96 | 2021 |
| Multi-modal image annotation with multi-instance multi-label LDA CT Nguyen, DC Zhan, ZH Zhou Proceedings of the Twenty-Third international joint conference on Artificial …, 2013 | 88 | 2013 |
| 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 | 77 | 2021 |
| Learning embedding adaptation for few-shot learning HJ Ye, H Hu, DC Zhan, F Sha arXiv preprint arXiv:1812.03664 7, 2018 | 73 | 2018 |
| Pycil: A python toolbox for class-incremental learning DW Zhou, FY Wang, HJ Ye, DC Zhan Science China Information Sciences 66 (9), 197101, 2023 | 71 | 2023 |
| Complex object classification: A multi-modal multi-instance multi-label deep network with optimal transport Y Yang, YF Wu, DC Zhan, ZB Liu, Y Jiang Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018 | 67 | 2018 |
| Co-transport for class-incremental learning DW Zhou, HJ Ye, DC Zhan Proceedings of the 29th ACM International Conference on Multimedia, 1645-1654, 2021 | 63 | 2021 |
| 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 | 62 | 2024 |
| Learning instance specific distances using metric propagation DC Zhan, M Li, YF Li, ZH Zhou Proceedings of the 26th annual international conference on machine learning …, 2009 | 61 | 2009 |
Han-Jia YeNanjing UniversityVerified email at lamda.nju.edu.cn
