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Found 10 papers, 7 papers with code
UMMAFormer: A Universal Multimodal-adaptive Transformer Framework for Temporal Forgery Localization
Our approach introduces a Temporal Feature Abnormal Attention (TFAA) module based on temporal feature reconstruction to enhance the detection of temporal differences.
Ranked #1 on
Temporal Forgery Localization
on LAV-DF
Phase Retrieval with Background Information: Decreased References and Efficient Methods
Furthermore, two methods called Background Douglas-Rachford (BDR) and Convex Background Douglas-Rachford (CBDR) are proposed.
Untrained neural network embedded Fourier phase retrieval from few measurements
FPR with few measurements is important for reducing time and hardware costs, but it suffers from serious ill-posedness.
Regularize implicit neural representation by itself
The smoothness of the Laplacian matrix is further integrated by parameterizing DE with a tiny INR.
ADMM based Fourier phase retrieval with untrained generative prior
Then an untrained generative network is embedded in the iterative process of ADMM to project an estimated signal into the generative space, and the projected signal is applied to next iteration of ADMM.
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.
Fisher Matrix Based Fault Detection for PMUs Data in Power Grids
Firstly, the fault detection matrix is constructed and the event detection problem is reformatted as a two-sample covariance matrices test problem.
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 Robust graph attention network with dynamic adjusted Graph
Different from the original GAT, which uses the attention mechanism for different edges but is still sensitive to the perturbation, RoGAT adds an extra dynamic attention score progressively and improves the robustness.
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.
