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ORCIDZhen Fang 0001
Person information
- affiliation (PhD 2021): University of Technology Sydney, Australia
Other persons with the same name
- Zhen Fang — disambiguation page
- Zhen Fang 0002 — Nvidia Corporation, Santa Clara, USA (and 1 more)
- Zhen Fang 0003
![[0000-0002-2976-3242]](https://dblp1.uni-trier.de/img/orcid-mark.12x12.png "0000-0002-2976-3242")
— Chinese Academy of Sciences, Aerospace Information Research Institute, AIRCAS, Beijing, China (and 2 more)
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2020 – today
- 2025
[j13]Jun Nie, Yadan Luo, Shanshan Ye, Yonggang Zhang, Xinmei Tian, Zhen Fang:
Out-of-Distribution Detection with Virtual Outlier Smoothing. Int. J. Comput. Vis. 133(2): 724-741 (2025)- [j12]Xuhui Li, Zhen Fang, Yonggang Zhang, Ning Ma, Jiajun Bu, Bo Han, Haishuai Wang:
Characterizing Submanifold Region for Out-of-Distribution Detection. IEEE Trans. Knowl. Data Eng. 37(1): 130-147 (2025) - 2024
- [j11]Ningyuan Zhang, Jie Lu, Keqiuyin Li, Zhen Fang, Guangquan Zhang:
Source-Free Unsupervised Domain Adaptation: Current research and future directions. Neurocomputing 564: 126921 (2024) - [j10]Zhen Fang, Yixuan Li, Feng Liu, Bo Han, Jie Lu:
On the Learnability of Out-of-distribution Detection. J. Mach. Learn. Res. 25: 84:1-84:83 (2024) - [j9]Jiahua Dong, Yang Cong, Gan Sun, Zhen Fang, Zhengming Ding:
Where and How to Transfer: Knowledge Aggregation-Induced Transferability Perception for Unsupervised Domain Adaptation. IEEE Trans. Pattern Anal. Mach. Intell. 46(3): 1664-1681 (2024) - [j8]Guangzhi Ma, Jie Lu, Feng Liu, Zhen Fang, Guangquan Zhang:
Multiclass Classification With Fuzzy-Feature Observations: Theory and Algorithms. IEEE Trans. Cybern. 54(2): 1048-1061 (2024) - [j7]Zhen Fang, Jie Lu, Guangquan Zhang:
An Extremely Simple Algorithm for Source Domain Reconstruction. IEEE Trans. Cybern. 54(3): 1921-1933 (2024) - [j6]Guangzhi Ma, Jie Lu, Feng Liu, Zhen Fang, Guangquan Zhang:
Domain Adaptation With Interval-Valued Observations: Theory and Algorithms. IEEE Trans. Fuzzy Syst. 32(5): 3107-3120 (2024) - [j5]Kuo Shi, Jie Lu, Zhen Fang, Guangquan Zhang:
Unsupervised Domain Adaptation Enhanced by Fuzzy Prompt Learning. IEEE Trans. Fuzzy Syst. 32(7): 4038-4048 (2024) - [c31]Kuo Shi, Jie Lu, Zhen Fang, Guangquan Zhang:
Enhancing Vision-Language Models Incorporating TSK Fuzzy System for Domain Adaptation. FUZZ 2024: 1-8 - [c30]Xuefeng Du, Zhen Fang, Ilias Diakonikolas, Yixuan Li:
How Does Unlabeled Data Provably Help Out-of-Distribution Detection? ICLR 2024 - [c29]Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han:
Negative Label Guided OOD Detection with Pretrained Vision-Language Models. ICLR 2024 - [c28]Jun Nie, Yonggang Zhang, Zhen Fang, Tongliang Liu, Bo Han, Xinmei Tian:
Out-of-Distribution Detection with Negative Prompts. ICLR 2024 - [c27]Bo Peng, Yadan Luo, Yonggang Zhang, Yixuan Li, Zhen Fang:
ConjNorm: Tractable Density Estimation for Out-of-Distribution Detection. ICLR 2024 - [c26]Pengfei Zheng, Yonggang Zhang, Zhen Fang, Tongliang Liu, Defu Lian, Bo Han:
NoiseDiffusion: Correcting Noise for Image Interpolation with Diffusion Models beyond Spherical Linear Interpolation. ICLR 2024 - [c25]Bo Peng, Zhen Fang, Guangquan Zhang, Jie Lu:
Knowledge Distillation with Auxiliary Variable. ICML 2024 - [c24]Kuo Shi, Jie Lu, Zhen Fang, Guangquan Zhang:
CLIP-Enhanced Unsupervised Domain Adaptation with Consistency Regularization. IJCNN 2024: 1-8 - [c23]Ran Wang, Hua Zuo, Zhen Fang, Jie Lu:
Prompt-Based Memory Bank for Continual Test-Time Domain Adaptation in Vision-Language Models. IJCNN 2024: 1-8 - [c22]Chenrui Wu, Haishuai Wang, Xiang Zhang, Zhen Fang, Jiajun Bu:
Spatio-temporal Heterogeneous Federated Learning for Time Series Classification with Multi-view Orthogonal Training. ACM Multimedia 2024: 2613-2622 - [c21]Ran Wang, Hua Zuo, Zhen Fang, Jie Lu:
Towards Robustness Prompt Tuning with Fully Test-Time Adaptation for CLIP's Zero-Shot Generalization. ACM Multimedia 2024: 8604-8612 - [c20]Yonggang Zhang, Jie Lu, Bo Peng, Zhen Fang, Yiu-ming Cheung:
Learning to Shape In-distribution Feature Space for Out-of-distribution Detection. NeurIPS 2024 - [i24]Xuefeng Du, Zhen Fang, Ilias Diakonikolas, Yixuan Li:
How Does Unlabeled Data Provably Help Out-of-Distribution Detection? CoRR abs/2402.03502 (2024) - [i23]Bo Peng, Yadan Luo, Yonggang Zhang, Yixuan Li, Zhen Fang:
ConjNorm: Tractable Density Estimation for Out-of-Distribution Detection. CoRR abs/2402.17888 (2024) - [i22]Pengfei Zheng, Yonggang Zhang, Zhen Fang, Tongliang Liu, Defu Lian, Bo Han:
NoiseDiffusion: Correcting Noise for Image Interpolation with Diffusion Models beyond Spherical Linear Interpolation. CoRR abs/2403.08840 (2024) - [i21]Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han:
Negative Label Guided OOD Detection with Pretrained Vision-Language Models. CoRR abs/2403.20078 (2024) - [i20]Zhen Fang, Yixuan Li, Feng Liu, Bo Han, Jie Lu:
On the Learnability of Out-of-distribution Detection. CoRR abs/2404.04865 (2024) - [i19]Feng Gu, Jie Lu, Zhen Fang, Kun Wang, Guangquan Zhang:
A Neighbor-Searching Discrepancy-based Drift Detection Scheme for Learning Evolving Data. CoRR abs/2405.14153 (2024) - [i18]Yicheng Wang, Feng Liu, Junmin Liu, Zhen Fang, Kai Sun:
Exclusive Style Removal for Cross Domain Novel Class Discovery. CoRR abs/2406.18140 (2024) - 2023
- [j4]Zhen Fang, Jie Lu, Feng Liu, Guangquan Zhang:
Semi-Supervised Heterogeneous Domain Adaptation: Theory and Algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 45(1): 1087-1105 (2023) - [j3]Zhong Li, Zhen Fang, Feng Liu, Bo Yuan, Guangquan Zhang, Jie Lu:
Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation. IEEE Trans. Neural Networks Learn. Syst. 34(8): 3859-3873 (2023) - [c19]Ran Wang, Hua Zuo, Zhen Fang, Jie Lu:
Multiple Teacher Model for Continual Test-Time Domain Adaptation. AI (1) 2023: 304-314 - [c18]Duzhen Zhang, Wei Cong, Jiahua Dong, Yahan Yu, Xiuyi Chen, Yonggang Zhang, Zhen Fang:
Continual Named Entity Recognition without Catastrophic Forgetting. EMNLP 2023: 8186-8197 - [c17]Yue Yang, Kairui Guo, Zhen Fang, Hua Lin, Mark Grosser, Jie Lu:
Multi-model Transfer Learning and Genotypic Analysis for Seizure Type Classification. HIS 2023: 223-234 - [c16]Yadan Luo, Zhuoxiao Chen, Zhen Fang, Zheng Zhang, Mahsa Baktashmotlagh, Zi Huang:
Kecor: Kernel Coding Rate Maximization for Active 3D Object Detection. ICCV 2023: 18233-18244 - [c15]Xinheng Wu, Jie Lu, Zhen Fang, Guangquan Zhang:
Meta OOD Learning For Continuously Adaptive OOD Detection. ICCV 2023: 19296-19307 - [c14]Rui Dai, Yonggang Zhang, Zhen Fang, Bo Han, Xinmei Tian:
Moderately Distributional Exploration for Domain Generalization. ICML 2023: 6786-6817 - [c13]Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han:
Detecting Out-of-distribution Data through In-distribution Class Prior. ICML 2023: 15067-15088 - [c12]Guohang Zeng, Zhen Fang, Guangquan Zhang, Jie Lu:
One-step Domain Adaptation Approach with Partial Label. IJCNN 2023: 1-8 - [c11]Qizhou Wang, Zhen Fang, Yonggang Zhang, Feng Liu, Yixuan Li, Bo Han:
Learning to Augment Distributions for Out-of-distribution Detection. NeurIPS 2023 - [c10]Zige Wang, Yonggang Zhang, Zhen Fang, Long Lan, Wenjing Yang, Bo Han:
SODA: Robust Training of Test-Time Data Adaptors. NeurIPS 2023 - [c9]Mengyue Yang, Yonggang Zhang, Zhen Fang, Yali Du, Furui Liu, Jean-Francois Ton, Jianhong Wang, Jun Wang:
Invariant Learning via Probability of Sufficient and Necessary Causes. NeurIPS 2023 - [c8]Haotian Zheng, Qizhou Wang, Zhen Fang, Xiaobo Xia, Feng Liu, Tongliang Liu, Bo Han:
Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources. NeurIPS 2023 - [i17]Rui Dai, Yonggang Zhang, Zhen Fang, Bo Han, Xinmei Tian:
Moderately Distributional Exploration for Domain Generalization. CoRR abs/2304.13976 (2023) - [i16]Yadan Luo, Zhuoxiao Chen, Zhen Fang, Zheng Zhang, Zi Huang, Mahsa Baktashmotlagh:
KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection. CoRR abs/2307.07942 (2023) - [i15]Xinheng Wu, Jie Lu, Zhen Fang, Guangquan Zhang:
Meta OOD Learning for Continuously Adaptive OOD Detection. CoRR abs/2309.11705 (2023) - [i14]Mengyue Yang, Zhen Fang, Yonggang Zhang, Yali Du, Furui Liu, Jean-Francois Ton, Jun Wang:
Invariant Learning via Probability of Sufficient and Necessary Causes. CoRR abs/2309.12559 (2023) - [i13]Zige Wang, Yonggang Zhang, Zhen Fang, Long Lan, Wenjing Yang, Bo Han:
SODA: Robust Training of Test-Time Data Adaptors. CoRR abs/2310.11093 (2023) - [i12]Duzhen Zhang, Wei Cong, Jiahua Dong, Yahan Yu, Xiuyi Chen, Yonggang Zhang, Zhen Fang:
Continual Named Entity Recognition without Catastrophic Forgetting. CoRR abs/2310.14541 (2023) - [i11]Qizhou Wang, Zhen Fang, Yonggang Zhang, Feng Liu, Yixuan Li, Bo Han:
Learning to Augment Distributions for Out-of-Distribution Detection. CoRR abs/2311.01796 (2023) - [i10]Haotian Zheng, Qizhou Wang, Zhen Fang, Xiaobo Xia, Feng Liu, Tongliang Liu, Bo Han:
Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources. CoRR abs/2311.03236 (2023) - 2022
- [j2]Yiyang Zhang, Feng Liu, Zhen Fang, Bo Yuan, Guangquan Zhang, Jie Lu:
Learning From a Complementary-Label Source Domain: Theory and Algorithms. IEEE Trans. Neural Networks Learn. Syst. 33(12): 7667-7681 (2022) - [c7]Jiahua Dong, Lixu Wang, Zhen Fang, Gan Sun, Shichao Xu, Xiao Wang, Qi Zhu:
Federated Class-Incremental Learning. CVPR 2022: 10154-10163 - [c6]Zhen Fang, Yixuan Li, Jie Lu, Jiahua Dong, Bo Han, Feng Liu:
Is Out-of-Distribution Detection Learnable? NeurIPS 2022 - [i9]Jiahua Dong, Lixu Wang, Zhen Fang, Gan Sun, Shichao Xu, Xiao Wang, Qi Zhu:
Federated Class-Incremental Learning. CoRR abs/2203.11473 (2022) - [i8]Guangzhi Ma, Jie Lu, Feng Liu, Zhen Fang, Guangquan Zhang:
Multi-class Classification with Fuzzy-feature Observations: Theory and Algorithms. CoRR abs/2206.04311 (2022) - [i7]Zhen Fang, Yixuan Li, Jie Lu, Jiahua Dong, Bo Han, Feng Liu:
Is Out-of-Distribution Detection Learnable? CoRR abs/2210.14707 (2022) - 2021
- [b1]Zhen Fang:
Bridging Theory and Algorithms for Open-Set and Heterogeneous Domain Adaptations. University of Technology Sydney, Australia, 2021 - [j1]Zhen Fang, Jie Lu, Feng Liu, Junyu Xuan, Guangquan Zhang:
Open Set Domain Adaptation: Theoretical Bound and Algorithm. IEEE Trans. Neural Networks Learn. Syst. 32(10): 4309-4322 (2021) - [c5]Zhong Li, Zhen Fang, Feng Liu, Jie Lu, Bo Yuan, Guangquan Zhang:
How Does the Combined Risk Affect the Performance of Unsupervised Domain Adaptation Approaches? AAAI 2021: 11079-11087 - [c4]Zhen Fang, Jie Lu, Anjin Liu, Feng Liu, Guangquan Zhang:
Learning Bounds for Open-Set Learning. ICML 2021: 3122-3132 - [c3]Jiahua Dong, Zhen Fang, Anjin Liu, Gan Sun, Tongliang Liu:
Confident Anchor-Induced Multi-Source Free Domain Adaptation. NeurIPS 2021: 2848-2860 - [i6]Zhong Li, Zhen Fang, Feng Liu, Jie Lu, Bo Yuan, Guangquan Zhang:
How does the Combined Risk Affect the Performance of Unsupervised Domain Adaptation Approaches? CoRR abs/2101.01104 (2021) - [i5]Zhen Fang, Jie Lu, Anjin Liu, Feng Liu, Guangquan Zhang:
Learning Bounds for Open-Set Learning. CoRR abs/2106.15792 (2021) - 2020
- [c2]Yiyang Zhang, Feng Liu, Zhen Fang, Bo Yuan, Guangquan Zhang, Jie Lu:
Clarinet: A One-step Approach Towards Budget-friendly Unsupervised Domain Adaptation. IJCAI 2020: 2526-2532 - [i4]Zhong Li, Zhen Fang, Feng Liu, Bo Yuan, Guangquan Zhang, Jie Lu:
Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation. CoRR abs/2006.13022 (2020) - [i3]Yiyang Zhang, Feng Liu, Zhen Fang, Bo Yuan, Guangquan Zhang, Jie Lu:
Clarinet: A One-step Approach Towards Budget-friendly Unsupervised Domain Adaptation. CoRR abs/2007.14612 (2020) - [i2]Yiyang Zhang, Feng Liu, Zhen Fang, Bo Yuan, Guangquan Zhang, Jie Lu:
Learning from a Complementary-label Source Domain: Theory and Algorithms. CoRR abs/2008.01454 (2020)
2010 – 2019
- 2019
- [c1]Zhen Fang, Jie Lu, Feng Liu, Guangquan Zhang:
Unsupervised Domain Adaptation with Sphere Retracting Transformation. IJCNN 2019: 1-8 - [i1]Zhen Fang, Jie Lu, Feng Liu, Junyu Xuan, Guangquan Zhang:
Open Set Domain Adaptation: Theoretical Bound and Algorithm. CoRR abs/1907.08375 (2019)
Coauthor Index
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