| Open set domain adaptation: Theoretical bound and algorithm Z Fang, J Lu, F Liu, J Xuan, G Zhang IEEE Transactions on Neural Networks and Learning Systems, 2020 | 197 | 2020 |
| Federated Class-Incremental Learning J Dong, L Wang, Z Fang, G Sun, S Xu, X Wang, Q Zhu CVPR2022, 2022 | 171 | 2022 |
| Where and how to transfer: Knowledge aggregation-induced transferability perception for unsupervised domain adaptation J Dong, Y Cong, G Sun, Z Fang, Z Ding IEEE Transactions on Pattern Analysis and Machine Intelligence 46 (3), 1664-1681, 2021 | 152 | 2021 |
| Is Out-of-Distribution Detection Learnable? Z Fang, Y Li, J Lu, J Dong, B Han, F Liu NeurIPS 2022 Outstanding Paper Award, 2022 | 127 | 2022 |
| Bridging the theoretical bound and deep algorithms for open set domain adaptation L Zhong, Z Fang, F Liu, B Yuan, G Zhang, J Lu IEEE transactions on neural networks and learning systems 34 (8), 3859-3873, 2021 | 101 | 2021 |
| Confident Anchor-Induced Multi-Source Free Domain Adaptation J Dong, Z Fang, A Liu, G Sun, T Liu NeurIPS 2021, https://proceedings.neurips.cc/paper/202, 2021 | 92 | 2021 |
| Learning from a complementary-label source domain: theory and algorithms Y Zhang, F Liu, Z Fang, B Yuan, G Zhang, J Lu IEEE Transactions on Neural Networks and Learning Systems 33 (12), 7667-7681, 2021 | 91 | 2021 |
| Semi-supervised Heterogeneous Domain Adaptation: Theory and Algorithms Z Fang, J Lu, F Liu, G Zhang IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 | 69 | 2022 |
| Learning bounds for open-set learning Z Fang, J Lu, A Liu, F Liu, G Zhang International conference on machine learning, 3122-3132, 2021 | 63 | 2021 |
| How does the Combined Risk Affect the Performance of Unsupervised Domain Adaptation Approaches? L Zhong, Z Fang, F Liu, J Lu, B Yuan, G Zhang Accepted By AAAI 2021, 2020 | 56 | 2020 |
| Clarinet: A one-step approach towards budget-friendly unsupervised domain adaptation Y Zhang, F Liu, Z Fang, B Yuan, G Zhang, J Lu accepted by IJCAI 2020., 2020 | 40 | 2020 |
| Learning to augment distributions for out-of-distribution detection Q Wang, Z Fang, Y Zhang, F Liu, Y Li, B Han NeurIPS 2023, 2023 | 30 | 2023 |
| Negative Label Guided OOD Detection with Pretrained Vision-Language Models X Jiang, F Liu, Z Fang, H Chen, T Liu, F Zheng, B Han The Twelfth International Conference on Learning Representations （ICLR 2024）, 2024 | 24 | 2024 |
| Detecting Out-of-distribution Data through In-distribution Class Prior X Jiang, F Liu, Z Fang, H Chen, T Liu, F Zheng, B Han International Conference on Machine Learning (ICML 2023), 2023 | 18 | 2023 |
| Out-of-Distribution Detection with Negative Prompts J Nie, Y Zhang, Z Fang, T Liu, B Han, X Tian The Twelfth International Conference on Learning Representations （ICLR 2024）, 2024 | 17 | 2024 |
| Moderately Distributional Exploration for Domain Generalization R Dai, Y Zhang, Z Fang, B Han, X Tian International Conference on Machine Learning (ICML 2023), 2023 | 17 | 2023 |
| Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources H Zheng, Q Wang, Z Fang, X Xia, F Liu, T Liu, B Han NeurIPS 2023, 2023 | 16 | 2023 |
| Source-free unsupervised domain adaptation: Current research and future directions N Zhang, J Lu, K Li, Z Fang, G Zhang Neurocomputing 564, 126921, 2024 | 15 | 2024 |
| How Does Wild Data Provably Help OOD Detection? X Du, Z Fang, I Diakonikolas, Y Li The Twelfth International Conference on Learning Representations, 2024 | 14* | 2024 |
| Unsupervised domain adaptation with sphere retracting transformation Z Fang, J Lu, F Liu, G Zhang 2019 International Joint Conference on Neural Networks (IJCNN), 1-8, 2019 | 14 | 2019 |
Jie LU AO, FIEEE, FIFSA, FACS, Austr...Distinguished Professor, Director of Australian Artificial Intelligence InstituteVerified email at uts.edu.au
