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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.