Data-driven dimension reduction extracts a low-dimensional basis from sampled Green's functions to solve new multiscale elliptic PDEs with random coefficients efficiently.
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Integrating total variation regularization into U-Net and SegNet yields segmentation results with improved spatial regularity and noise robustness on WBC, CamVid, and SUN-RGBD datasets.
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A data-driven approach for multiscale elliptic PDEs with random coefficients based on intrinsic dimension reduction
Data-driven dimension reduction extracts a low-dimensional basis from sampled Green's functions to solve new multiscale elliptic PDEs with random coefficients efficiently.
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A Regularized Convolutional Neural Network for Semantic Image Segmentation
Integrating total variation regularization into U-Net and SegNet yields segmentation results with improved spatial regularity and noise robustness on WBC, CamVid, and SUN-RGBD datasets.