| 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 | 364* | 2020 |
| Fast generalization rates for distance metric learning HJ Ye, DC Zhan, Y Jiang Machine Learning 108 (2), 267-295, 2019 | 35 | 2019 |
| 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 | 34 | 2016 |
| 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 | 33* | 2021 |
| Learning placeholders for open-set recognition DW Zhou, HJ Ye, DC Zhan Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 33 | 2021 |
| 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 |
| Identifying and compensating for feature deviation in imbalanced deep learning HJ Ye, HY Chen, DC Zhan, WL Chao arXiv preprint arXiv:2001.01385, 2020 | 26 | 2020 |
| College student scholarships and subsidies granting: A multi-modal multi-label approach HJ Ye, DC Zhan, X Li, ZC Huang, Y Jiang 2016 IEEE 16th International Conference on Data Mining (ICDM), 559-568, 2016 | 23 | 2016 |
| 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 | 21 | 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 | 20 | 2020 |
| Learning Mahalanobis Distance Metric: Considering Instance Disturbance Helps. HJ Ye, DC Zhan, XM Si, Y Jiang IJCAI, 3315-3321, 2017 | 20 | 2017 |
| Revisiting meta-learning as supervised learning WL Chao, HJ Ye, DC Zhan, M Campbell, KQ Weinberger arXiv preprint arXiv:2002.00573, 2020 | 19 | 2020 |
| 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 | 17 | 2020 |
| Rectify heterogeneous models with semantic mapping HJ Ye, DC Zhan, Y Jiang, ZH Zhou International Conference on Machine Learning, 5630-5639, 2018 | 16 | 2018 |
| Auxiliary information regularized machine for multiple modality feature learning Y Yang, HJ Ye, DC Zhan, Y Jiang Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015 | 15 | 2015 |
| Multi-instance learning with emerging novel class XS Wei, HJ Ye, X Mu, J Wu, C Shen, ZH Zhou IEEE Transactions on Knowledge and Data Engineering 33 (5), 2109-2120, 2019 | 14 | 2019 |
| Few-shot learning with a strong teacher HJ Ye, L Ming, DC Zhan, WL Chao IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 | 10* | 2022 |
| Learning multiple local metrics: Global consideration helps HJ Ye, DC Zhan, N Li, Y Jiang IEEE transactions on pattern analysis and machine intelligence 42 (7), 1698-1712, 2019 | 9 | 2019 |
| Tailoring embedding function to heterogeneous few-shot tasks by global and local feature adaptors S Lu, HJ Ye, DC Zhan Proceedings of the AAAI Conference on Artificial Intelligence 35 (10), 8776-8783, 2021 | 8 | 2021 |
| Task cooperation for semi-supervised few-shot learning HJ Ye, XC Li, DC Zhan Proceedings of the AAAI conference on artificial intelligence 35 (12), 10682 …, 2021 | 8 | 2021 |
De-Chuan ZhanNanjing University, ChinaVerified email at nju.edu.cn
