Feng Liu

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 60

Top Topics

Transfer Learning

Multiclass Classification

Domain Adaptation

Top Venues

IEEE Trans. Neural Networks Learn. Syst.

Publications

Guangzhi Ma, Jie Lu, Feng Liu, Zhen Fang, Guangquan Zhang
Multiclass Classification With Fuzzy-Feature Observations: Theory and Algorithms. IEEE Trans. Cybern. 54 (2) (2024)
Hongduan Tian, Feng Liu, Tongliang Liu, Bo Du, Yiu-ming Cheung, Bo Han
MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing Optimized Kernel Dependence. CoRR (2024)
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 (2024)
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)
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) (2024)
Haoang Chi, Wenjing Yang, Feng Liu, Long Lan, Tao Qin, Bo Han
Does Confusion Really Hurt Novel Class Discovery? Int. J. Comput. Vis. 132 (8) (2024)
Yicheng Wang, Feng Liu, Junmin Liu, Zhen Fang, Kai Sun
Exclusive Style Removal for Cross Domain Novel Class Discovery. CoRR (2024)
Zhen Fang, Yixuan Li, Feng Liu, Bo Han, Jie Lu
On the Learnability of Out-of-distribution Detection. CoRR (2024)
Wenjie Wang, Biwei Huang, Feng Liu, Xinge You, Tongliang Liu, Kun Zhang, Mingming Gong
Optimal Kernel Choice for Score Function-based Causal Discovery. CoRR (2024)
Yiliao Song, Tingru Cui, Feng Liu
Designing Fair AI Systems: How Explanation specificity Influences Usersâ€™ Perceived Fairness and Trusting Intentions. ECIS (2023)
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 (2023)
Qizhou Wang, Junjie Ye, Feng Liu, Quanyu Dai, Marcus Kalander, Tongliang Liu, Jianye Hao, Bo Han
Out-of-distribution Detection with Implicit Outlier Transformation. ICLR (2023)
Li Zhong, 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) (2023)
Chenhong Zhou, Feng Liu, Chen Gong, Rongfei Zeng, Tongliang Liu, Kwok-Wai Cheung, Bo Han
KRADA: Known-region-aware Domain Alignment for Open-set Domain Adaptation in Semantic Segmentation. Trans. Mach. Learn. Res. 2023 (2023)
Xunye Tian, Feng Liu
Take a Close Look at the Optimization of Deep Kernels for Non-parametric Two-Sample Tests. ADC (2023)
Qizhou Wang, Zhen Fang, Yonggang Zhang, Feng Liu, Yixuan Li, Bo Han
Learning to Augment Distributions for Out-of-Distribution Detection. CoRR (2023)
Zhen Fang, Jie Lu, Feng Liu, Guangquan Zhang
Semi-Supervised Heterogeneous Domain Adaptation: Theory and Algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 45 (1) (2023)
Fan Dong, Jie Lu, Yiliao Song, Feng Liu, Guangquan Zhang
A Drift Region-Based Data Sample Filtering Method. IEEE Trans. Cybern. 52 (9) (2022)
Zhen Fang, Yixuan Li, Jie Lu, Jiahua Dong, Bo Han, Feng Liu
Is Out-of-Distribution Detection Learnable? CoRR (2022)
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) (2022)
Guangzhi Ma, Jie Lu, Feng Liu, Zhen Fang, Guangquan Zhang
Multi-class Classification with Fuzzy-feature Observations: Theory and Algorithms. CoRR (2022)
Xiong Peng, Feng Liu, Jingfen Zhang, Long Lan, Junjie Ye, Tongliang Liu, Bo Han
Bilateral Dependency Optimization: Defending Against Model-inversion Attacks. CoRR (2022)
Qizhou Wang, Feng Liu, Yonggang Zhang, Jing Zhang, Chen Gong, Tongliang Liu, Bo Han
Watermarking for Out-of-distribution Detection. NeurIPS (2022)
Ruize Gao, Jiongxiao Wang, Kaiwen Zhou, Feng Liu, Binghui Xie, Gang Niu, Bo Han, James Cheng
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack. CoRR (2022)
Ruize Gao, Jiongxiao Wang, Kaiwen Zhou, Feng Liu, Binghui Xie, Gang Niu, Bo Han, James Cheng
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack. ICML (2022)
Zhen Fang, Yixuan Li, Jie Lu, Jiahua Dong, Bo Han, Feng Liu
Is Out-of-Distribution Detection Learnable? NeurIPS (2022)
Zhen Fang, Jie Lu, Anjin Liu, Feng Liu, Guangquan Zhang
Learning Bounds for Open-Set Learning. ICML (2021)
Li Zhong, 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)
Feng Liu, Wenkai Xu, Jie Lu, Danica J. Sutherland
Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data. NeurIPS (2021)
Qizhou Wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama
Probabilistic Margins for Instance Reweighting in Adversarial Training. CoRR (2021)
Zhen Fang, Jie Lu, Anjin Liu, Feng Liu, Guangquan Zhang
Learning Bounds for Open-Set Learning. CoRR (2021)
Li Zhong, Zhen Fang, Feng Liu, Jie Lu, Bo Yuan, Guangquan Zhang
How does the Combined Risk Affect the Performance of Unsupervised Domain Adaptation Approaches? CoRR (2021)
Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Gang Niu, Bo Han
Meta Discovery: Learning to Discover Novel Classes given Very Limited Data. CoRR (2021)
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) (2021)
Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, William K. Cheung, James T. Kwok
TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation. NeurIPS (2021)
Guangzhi Ma, Feng Liu, Guangquan Zhang, Jie Lu
Learning from Imprecise Observations: An Estimation Error Bound based on Fuzzy Random Variables. FUZZ-IEEE (2021)
Qizhou Wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama
Probabilistic Margins for Instance Reweighting in Adversarial Training. NeurIPS (2021)
Feng Liu, Guangquan Zhang, Jie Lu
Multisource Heterogeneous Unsupervised Domain Adaptation via Fuzzy Relation Neural Networks. IEEE Trans. Fuzzy Syst. 29 (11) (2021)
Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Masashi Sugiyama
Maximum Mean Discrepancy is Aware of Adversarial Attacks. CoRR (2020)
Feng Liu, Wenkai Xu, Jie Lu, Guangquan Zhang, Arthur Gretton, Danica J. Sutherland
Learning Deep Kernels for Non-Parametric Two-Sample Tests. ICML (2020)
Feng Liu, Wenkai Xu, Jie Lu, Guangquan Zhang, Arthur Gretton, Danica J. Sutherland
Learning Deep Kernels for Non-Parametric Two-Sample Tests. CoRR (2020)
Yiyang Zhang, Feng Liu, Zhen Fang, Bo Yuan, Guangquan Zhang, Jie Lu
Learning from a Complementary-label Source Domain: Theory and Algorithms. CoRR (2020)
Feng Liu, Guangquan Zhang, Jie Lu
Heterogeneous Domain Adaptation: An Unsupervised Approach. IEEE Trans. Neural Networks Learn. Syst. 31 (12) (2020)
Feng Liu, Guangquan Zhang, Jie Lu
A Novel Non-parametric Two-Sample Test on Imprecise Observations. FUZZ-IEEE (2020)
Yiyang Zhang, Feng Liu, Zhen Fang, Bo Yuan, Guangquan Zhang, Jie Lu
Clarinet: A One-step Approach Towards Budget-friendly Unsupervised Domain Adaptation. CoRR (2020)
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)
Li Zhong, Zhen Fang, Feng Liu, Bo Yuan, Guangquan Zhang, Jie Lu
Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation. CoRR (2020)
Zhen Fang, Jie Lu, Feng Liu, Junyu Xuan, Guangquan Zhang
Open Set Domain Adaptation: Theoretical Bound and Algorithm. CoRR (2019)
Zhen Fang, Jie Lu, Feng Liu, Guangquan Zhang
Unsupervised Domain Adaptation with Sphere Retracting Transformation. IJCNN (2019)
Feng Liu, Jie Lu, Bo Han, Gang Niu, Guangquan Zhang, Masashi Sugiyama
Butterfly: A Panacea for All Difficulties in Wildly Unsupervised Domain Adaptation. CoRR (2019)
Hua Zuo, Jie Lu, Guangquan Zhang, Feng Liu
Fuzzy Transfer Learning Using an Infinite Gaussian Mixture Model and Active Learning. IEEE Trans. Fuzzy Syst. 27 (2) (2019)
Feng Liu, Guangquan Zhang, Jie Lu
A Novel Fuzzy Neural Network for Unsupervised Domain Adaptation in Heterogeneous Scenarios. FUZZ-IEEE (2019)
Feng Liu, Jie Lu, Guangquan Zhang
Unsupervised Heterogeneous Domain Adaptation via Shared Fuzzy Equivalence Relations. IEEE Trans. Fuzzy Syst. 26 (6) (2018)
Yi Zhang, Jie Lu, Feng Liu, Qian Liu, Alan L. Porter, Hongshu Chen, Guangquan Zhang
Does deep learning help topic extraction? A kernel k-means clustering method with word embedding. J. Informetrics 12 (4) (2018)
Feng Liu, Guangquan Zhang, Jie Lu
Unconstrained fuzzy feature fusion for heterogeneous unsupervised domain adaptation. FUZZ-IEEE (2018)
Anjin Liu, Jie Lu, Feng Liu, Guangquan Zhang
Accumulating regional density dissimilarity for concept drift detection in data streams. Pattern Recognit. 76 (2018)
Feng Liu, Guangquan Zhang, Haiyan Lu, Jie Lu
Heterogeneous Unsupervised Cross-domain Transfer Learning. CoRR (2017)
Qian Zhang, Dianshuang Wu, Jie Lu, Feng Liu, Guangquan Zhang
A cross-domain recommender system with consistent information transfer. Decis. Support Syst. 104 (2017)
Feng Liu, Guangquan Zhang, Jie Lu
Heterogeneous unsupervised domain adaptation based on fuzzy feature fusion. FUZZ-IEEE (2017)
Jianzhou Wang, Feng Liu, Yiliao Song, Jing Zhao
A novel model: Dynamic choice artificial neural network (DCANN) for an electricity price forecasting system. Appl. Soft Comput. 48 (2016)