no code implementations • 9 Feb 2024 • Kecheng Chen, Elena Gal, Hong Yan, Haoliang Li
In this work, we propose to tackle the problem of domain generalization in the context of \textit{insufficient samples}.
no code implementations • 14 Aug 2023 • Ziru Liu, Kecheng Chen, Fengyi Song, Bo Chen, Xiangyu Zhao, Huifeng Guo, Ruiming Tang
In the domain of streaming recommender systems, conventional methods for addressing new user IDs or item IDs typically involve assigning initial ID embeddings randomly.
no code implementations • 15 Jun 2023 • Kecheng Chen, Hiroshi Kogi, Kenichi Soga
Traditional vision-based monitoring can directly capture an extensive range of motion but cannot separate the tunnel's vibration and deformation mode.
1 code implementation • 26 Feb 2023 • Kecheng Chen, Jie Liu, Renjie Wan, Victor Ho-Fun Lee, Varut Vardhanabhuti, Hong Yan, Haoliang Li
To address these issues, we propose to leverage a probabilistic reconstruction framework to conduct a joint discrepancy minimization between source and target domains in both the latent and image spaces.
no code implementations • 9 Sep 2021 • Peng Yi, Kecheng Chen, Zhaoqi Ma, Di Zhao, Xiaorong Pu, Yazhou Ren
To address this issue, we propose a novel 1-D EEG signal denoising network with 2-D transformer, namely EEGDnet.
3 code implementations • 15 May 2021 • Kecheng Chen, Jiayu Sun, Jiang Shen, Jixiang Luo, Xinyu Zhang, Xuelin Pan, Dongsheng Wu, Yue Zhao, Miguel Bento, Yazhou Ren, Xiaorong Pu
To address this issue, we propose a novel graph convolutional network-based LDCT denoising model, namely GCN-MIF, to explicitly perform multi-information fusion for denoising purpose.
no code implementations • 18 Apr 2021 • Kecheng Chen, Kun Long, Yazhou Ren, Jiayu Sun, Xiaorong Pu
To this end, we propose a play-and-plug medical image denoising framework, namely Lesion-Inspired Denoising Network (LIDnet), to collaboratively improve both denoising performance and detection accuracy of denoised medical images.