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Cascade Deep Networks for Sparse Linear Inverse Problems

Zhang, Huan, Shi, Hong and Wang, Wenwu (2018) Cascade Deep Networks for Sparse Linear Inverse Problems In: International Conference on Pattern Recognition (ICPR 2018), August 20-24, 2018, Beijing, China.

Cascade Deep Networks for Sparse Linear Inverse Problems.pdf - Accepted version Manuscript

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Sparse deep networks have been widely used in many linear inverse problems, such as image super-resolution and signal recovery. Its performance is as good as deep learning at the same time its parameters are much less than deep learning. However, when the linear inverse problems involve several linear transformations or the ratio of input dimension to output dimension is large, the performance of a single sparse deep network is poor. In this paper, we propose a cascade sparse deep network to address the above problem. In our model, we trained two cascade sparse networks based on Gregor and LeCun’s “learned ISTA” and “learned CoD”. The cascade structure can effectively improve the performance as compared to the noncascade model. We use the proposed methods in image sparse code prediction and signal recovery. The experimental results show that both algorithms perform favorably against a single sparse network.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Zhang, Huan
Shi, Hong
Date : 20 August 2018
Copyright Disclaimer : Copyright 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Related URLs :
Depositing User : Clive Harris
Date Deposited : 19 Sep 2018 08:35
Last Modified : 19 Sep 2018 08:35

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