| 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 | 542 | 2005 |
| 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 | 449 | 2020 |
| Semi-supervised learning with very few labeled training examples ZH Zhou, DC Zhan, Q Yang AAAI, 675-680, 2007 | 210 | 2007 |
| Learning placeholders for open-set recognition DW Zhou, HJ Ye, DC Zhan Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 92 | 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 | 83 | 2013 |
| 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 | 62 | 2009 |
| Learning embedding adaptation for few-shot learning HJ Ye, H Hu, DC Zhan, F Sha arXiv preprint arXiv:1812.03664 7, 2018 | 54 | 2018 |
| A novel serial multimodal biometrics framework based on semisupervised learning techniques Q Zhang, Y Yin, DC Zhan, J Peng IEEE transactions on information forensics and security 9 (10), 1681-1694, 2014 | 45 | 2014 |
| Deep learning for fixed model reuse Y Yang, DC Zhan, Y Fan, Y Jiang, ZH Zhou Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 44 | 2017 |
| Learning adaptive classifiers synthesis for generalized few-shot learning HJ Ye, H Hu, DC Zhan International Journal of Computer Vision 129, 1930-1953, 2021 | 42 | 2021 |
| Identifying and compensating for feature deviation in imbalanced deep learning HJ Ye, HY Chen, DC Zhan, WL Chao arXiv preprint arXiv:2001.01385, 2020 | 40 | 2020 |
| 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 | 40 | 2016 |
| 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 | 39 | 2019 |
| 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 | 37 | 2018 |
| Fei Sha. Learning embedding adaptation for few-shot learning HJ Ye, H Hu, DC Zhan arXiv preprint arXiv:1812.03664 2 (8), 2018 | 37 | 2018 |
| 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 | 36 | 2019 |
| 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 | 33 | 2022 |
| Semi-Supervised Multi-Modal Learning with Incomplete Modalities. Y Yang, DC Zhan, XR Sheng, Y Jiang IJCAI, 2998-3004, 2018 | 30 | 2018 |
| 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 | 30 | 2015 |
| Few-shot learning with adaptively initialized task optimizer: a practical meta-learning approach HJ Ye, XR Sheng, DC Zhan Machine Learning 109, 643-664, 2020 | 28 | 2020 |
Han-Jia YeNanjing UniversityVerified email at lamda.nju.edu.cn
