Re-Evaluating the Impact of Unseen-Class Unlabeled Data on Semi-Supervised Learning Model.
CoRR, March, 2025
G-OSR: A Comprehensive Benchmark for Graph Open-Set Recognition.
CoRR, March, 2025
Diverse Teacher-Students for deep safe semi-supervised learning under class mismatch.
Neural Networks, 2025
Visual Out-of-Distribution Detection in Open-Set Noisy Environments.
Int. J. Comput. Vis., November, 2024
Correction: Learning sample-aware threshold for semi-supervised learning.
Mach. Learn., July, 2024
Learning sample-aware threshold for semi-supervised learning.
Mach. Learn., July, 2024
SAFER-STUDENT for Safe Deep Semi-Supervised Learning With Unseen-Class Unlabeled Data.
IEEE Trans. Knowl. Data Eng., January, 2024
CLIP-driven Outliers Synthesis for few-shot OOD detection.
CoRR, 2024
Discriminability-Driven Channel Selection for Out-of-Distribution Detection.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024
Navigating the Unknown: A Novel MGUAN Framework for Medical Image Recognition Across Dynamic Domains.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2024
Exploring Channel-Aware Typical Features for Out-of-Distribution Detection.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024
Neighborhood-based credibility anchor learning for universal domain adaptation.
Pattern Recognit., October, 2023
How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation.
CoRR, 2023
LHAct: Rectifying Extremely Low and High Activations for Out-of-Distribution Detection.
Proceedings of the 31st ACM International Conference on Multimedia, 2023
Topological Structure Learning for Weakly-Supervised Out-of-Distribution Detection.
Proceedings of the 31st ACM International Conference on Multimedia, 2023
MHPL: Minimum Happy Points Learning for Active Source Free Domain Adaptation.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023
Discriminability and Transferability Estimation: A Bayesian Source Importance Estimation Approach for Multi-Source-Free Domain Adaptation.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023
Towards safe and robust weakly-supervised anomaly detection under subpopulation shift.
Knowl. Based Syst., 2022
Topological Structure Learning for Weakly-Supervised Out-of-Distribution Detection.
CoRR, 2022
RONF: Reliable Outlier Synthesis under Noisy Feature Space for Out-of-Distribution Detection.
Proceedings of the MM '22: The 30th ACM International Conference on Multimedia, Lisboa, Portugal, October 10, 2022
Safe-Student for Safe Deep Semi-Supervised Learning with Unseen-Class Unlabeled Data.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022
SNAIL: Semi-Separated Uncertainty Adversarial Learning for Universal Domain Adaptation.
Proceedings of the Asian Conference on Machine Learning, 2022
Not All Parameters Should Be Treated Equally: Deep Safe Semi-supervised Learning under Class Distribution Mismatch.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022
Normality Learning in Multispace for Video Anomaly Detection.
IEEE Trans. Circuits Syst. Video Technol., 2021
Semi-Supervised Screening of COVID-19 from Positive and Unlabeled Data with Constraint Non-Negative Risk Estimator.
Proceedings of the Information Processing in Medical Imaging, 2021
Robust Anomaly Detection from Partially Observed Anomalies with Augmented Classes.
Proceedings of the Artificial Intelligence - First CAAI International Conference, 2021