Deep learning for compressive sensing: a ubiquitous systems perspective

被引:32
作者
Machidon, Alina L. [1 ]
Pejovic, Veljko [1 ,2 ]
机构
[1] Univ Ljubljana, Fac Comp & Informat Sci, Vecna Pot 113, Ljubljana, Slovenia
[2] Inst Jozef Stefan, Dept Comp Syst, Jamova Cesta 39, Ljubljana, Slovenia
关键词
Neural networks; Deep learning; Compressive sensing; Ubiquitous computing; CONVOLUTIONAL NEURAL-NETWORKS; ROBUST UNCERTAINTY PRINCIPLES; INVERSE PROBLEMS; THRESHOLDING ALGORITHM; SIGNAL RECONSTRUCTION; INTERNET; ADAPTATION; RECOVERY;
D O I
10.1007/s10462-022-10259-5
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Compressive sensing (CS) is a mathematically elegant tool for reducing the sensor sampling rate, potentially bringing context-awareness to a wider range of devices. Nevertheless, practical issues with the sampling and reconstruction algorithms prevent further proliferation of CS in real world domains, especially among heterogeneous ubiquitous devices. Deep learning (DL) naturally complements CS for adapting the sampling matrix, reconstructing the signal, and learning from the compressed samples. While the CS-DL integration has received substantial research interest recently, it has not yet been thoroughly surveyed, nor has any light been shed on practical issues towards bringing the CS-DL to real world implementations in the ubiquitous computing domain. In this paper we identify main possible ways in which CS and DL can interplay, extract key ideas for making CS-DL efficient, outline major trends in the CS-DL research space, and derive guidelines for the future evolution of CS-DL within the ubiquitous computing domain.
引用
收藏
页码:3619 / 3658
页数:40
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