no code implementations • 12 Feb 2024 • Qiuhao Zeng, Wei Wang, Fan Zhou, Gezheng Xu, Ruizhi Pu, Changjian Shui, Christian Gagne, Shichun Yang, Boyu Wang, Charles X. Ling
By employing Koopman Operators, we effectively address the time-evolving distributions encountered in TDG using the principles of Koopman theory, where measurement functions are sought to establish linear transition relations between evolving domains.
no code implementations • 31 Jan 2023 • Li Yi, Gezheng Xu, Pengcheng Xu, Jiaqi Li, Ruizhi Pu, Charles Ling, A. Ian McLeod, Boyu Wang
We also prove that such a difference makes existing LLN methods that rely on their distribution assumptions unable to address the label noise in SFDA.
no code implementations • 31 May 2022 • William Wei Wang, Gezheng Xu, Ruizhi Pu, Jiaqi Li, Fan Zhou, Changjian Shui, Charles Ling, Christian Gagné, Boyu Wang
Domain generalization aims to learn a predictive model from multiple different but related source tasks that can generalize well to a target task without the need of accessing any target data.
no code implementations • 29 Sep 2021 • Wei Wang, Jiaqi Li, Ruizhi Pu, Gezheng Xu, Fan Zhou, Changjian Shui, Charles Ling, Boyu Wang
Domain generalization aims to learn a predictive model from multiple different but related source tasks that can generalize well to a target task without the need of accessing any target data.
no code implementations • 14 Sep 2021 • Ruizhi Pu, Xinyu Zhang, Ruofei Lai, Zikai Guo, Yinxia Zhang, Hao Jiang, Yongkang Wu, Yantao Jia, Zhicheng Dou, Zhao Cao
Finally, supervisory signal in rear compressor is computed based on condition probability and thus can control sample dynamic and further enhance the model performance.