Exposure fusion based on sparse representation using approximate K SVD

Semantic segmentation based on fusion of features and classifiers

A novel selective ensemble algorithm based on approximate reducts and optimal sampling

论文摘要:在无监督域适应中,一个关键的研究问题是跨域的联合分布对齐 。 但是, 直接 对齐的源和目标联合分布是不可行的, 因为目标条件分布的标签未知。结合边缘分布对齐(它是条件分布对齐的等价条件,而不

Exposure

博客作者:凌逆战 论文地址:https://ieeexplore.ieee.xilesou.top/abstract/document/8683611/ 地址:https://www.cnblogs.

这篇论文提出了一种使用多任务学习生成通用、稳健的文本表示的方法，非常值得学习，欢迎下载！

fLRR: fast low-rank representation using Frobenius-norm

Cloud computing can provide a great capacity for massive computing, storage as well as processing. T

A Fast Algorithm for Recovery of Jointly Sparse Vectors based on

当窄带外辐射源数目稀少且空间分布不均匀时,通常会在无源雷达成像中产生稀疏的无规则空间谱填充,使得传统快速逆傅里叶方法(inverse fast Fourier transform,IFFT)或极坐标方