cvpr2020中的论文:Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution 论文连接arxiv.org/pdf/2003.07018.pdf 代码连接github.com/guoyongcs/DRN 论文摘要: 抛出了SISR中的两个限制: 1.学习从LR到HR图像的映射函数是一个典型的不适定问题,因为存在无限个HR图像可以下采样到相...
Thedes_normoption determines whether to variance-normalise the timecourses created by stage 1 of the dual regression; it is these that are used as the regressors in stage 2. If you don't normalise them, then you will only test for RSN "shape" in your cross-subject testing. If you do ...
《Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution》 从LR映射到HR是一个不适定问题,而且在现实中不容易获取大量的LR-HR成对的训练数据,且LR的退化方法是未知的。现有的超分辨方法还存在过拟合问题,导致在实际应用中性能低下。基于这些问题,该文章作者提出了双支路的回归网络,将LR...
Reliable intrinsic connectivity networks: Test–retest evaluation using ICA and dual regression approach Functional connectivity analyses of resting-state fMRI data are rapidly emerging as highly efficient and powerful tools for in vivo mapping of functional n... XN Zuo,C Kelly,JS Adelstein,... - ...
Projects Security Insights Additional navigation options master 1Branch1Tag Code README MIT license Pytorch implementation for "Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution". Important Update We released the code and models of ourDual Regression Compression (DRC)towards...
In the training stage, a double low-rank robust regression model (DLR) is proposed to learn the projection matrix. In DLR, low-rank robust regression motivates us to model the data as the sum of a low-rank clean data and sparse noise matrix. The low-rank is further used to constrain ...
ReMAPP: reverse multilateration based access point positioning using multivariate regression for indoor localization in smart buildings Indoor localization has attracted significant demand in diverse smart building applications like automated energy management, patient tracking in hospitals... PS Varma,V Anand...
Regression learning with non-identically and non-independently sampling A more general marginal distribution assumption is proposed. Under this assumption, the consistency of the regularization kernel network (RKN) and the ... HW Sun,MJ Zhang - 《International Journal of Wavelets Multiresolution & Inf...
Proposed by Tibshirani, the least absolute shrinkage and selection operator (LASSO) estimates a vector of regression coefficients by minimizing the residual sum of squares subject to a constraint on the l1-norm of the coefficient vector. The LASSO estimator typically has one or more zero elements ...
Findings from multiple regression analysis suggest that such placement renders two qualitatively different groups of retirees, one which is primarily concerned with health, and one for which financial adequacy is more important for retirement adjustment. Overall, it was concluded that structural components...