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Found 5 papers, 4 papers with code
Regularize implicit neural representation by itself
The smoothness of the Laplacian matrix is further integrated by parameterizing DE with a tiny INR.
Low-Rank Tensor Function Representation for Multi-Dimensional Data Recovery
To break this barrier, we propose a low-rank tensor function representation (LRTFR), which can continuously represent data beyond meshgrid with infinite resolution.
Adaptive and Implicit Regularization for Matrix Completion
Theoretically, we show that the adaptive regularization of \ReTwo{AIR} enhances the implicit regularization and vanishes at the end of training.
AIR-Net: Adaptive and Implicit Regularization Neural Network for Matrix Completion
Theoretically, we show that the adaptive regularization of AIR enhances the implicit regularization and vanishes at the end of training.
A regularized deep matrix factorized model of matrix completion for image restoration
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.
