Abstract:
Several recent studies have been conducted on solving the compressive sensing problem with deep learning framework , which enhance the signal recovery performance and gre...Show MoreMetadata
Abstract:
Several recent studies have been conducted on solving the compressive sensing problem with deep learning framework , which enhance the signal recovery performance and greatly shorten the running time compared with traditional algorithms. However, as the size of signals increases, so does the neural network, which will impose large memory space and high computational complexity, making it hard to use this method on mobile devices. To address this issue, in this paper, the neural network is decomposed by Tensor-Train (TT) format, which reduces the number of parameters to a great extent. In particular, the neural network decomposed by TT format is a stacked denoising autoencoder (SDA) network, which called TT-SDA. The experiments demonstrate that, especially with low measurement rates, the proposed TT-SDA network can improve the reconstruction results, reduce the computational complexity and save the memory space.
Date of Conference: 24-28 June 2019
Date Added to IEEE Xplore: 22 July 2019
ISBN Information: