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Found 4 papers, 1 papers with code
Learning Correction Filter via Degradation-Adaptive Regression for Blind Single Image Super-Resolution
Instead of assuming degradation are spatially invariant across the whole image, we learn correction filters to adjust degradations to known degradations in a spatially variant way by a novel linearly-assembled pixel degradation-adaptive regression module (DARM).
Data-driven and machine-learning based prediction of wave propagation behavior in dam-break flood
To show the performance of the RC-ESN model, we also provide a sensitivity analysis of the prediction accuracy concerning the key parameters including training set size, reservoir size, and spectral radius.
Learning Aligned Cross-Modal Representation for Generalized Zero-Shot Classification
Learning a common latent embedding by aligning the latent spaces of cross-modal autoencoders is an effective strategy for Generalized Zero-Shot Classification (GZSC).
Primal-Dual Active-Set Methods for Isotonic Regression and Trend Filtering
Isotonic regression (IR) is a non-parametric calibration method used in supervised learning.
