1 code implementation • CVPR 2023 • Zhemin Li, Hongxia Wang, Deyu Meng
The smoothness of the Laplacian matrix is further integrated by parameterizing DE with a tiny INR.
no code implementations • 1 Dec 2022 • YiSi Luo, XiLe Zhao, Zhemin Li, Michael K. Ng, Deyu Meng
To break this barrier, we propose a low-rank tensor function representation (LRTFR), which can continuously represent data beyond meshgrid with infinite resolution.
1 code implementation • 11 Aug 2022 • Zhemin Li, Tao Sun, Hongxia Wang, Bao Wang
Theoretically, we show that the adaptive regularization of \ReTwo{AIR} enhances the implicit regularization and vanishes at the end of training.
2 code implementations • 12 Oct 2021 • Zhemin Li, Tao Sun, Hongxia Wang, Bao Wang
Theoretically, we show that the adaptive regularization of AIR enhances the implicit regularization and vanishes at the end of training.
2 code implementations • 29 Jul 2020 • Zhemin Li, Zhi-Qin John Xu, Tao Luo, Hongxia Wang
In this work, we propose a Regularized Deep Matrix Factorized (RDMF) model for image restoration, which utilizes the implicit bias of the low rank of deep neural networks and the explicit bias of total variation.