# Image Classification with A Deep Network Model based on Compressive Sensing

Source: [Image Classification with A Deep Network Model based on Compressive Sensing](<https://www.sota2.com/research/paper/68fa61811e209131ce9f4baf>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T05:48:55.948Z

Authors: Yufei Gan, Tong Zhuo, Chu He

Published: 2014-09-25T15:52:05.000Z

## Abstract

To simplify the parameter of the deep learning network, a cascaded compressive sensing model "CSNet" is implemented for image classification. Firstly, we use cascaded compressive sensing network to learn feature from the data. Secondly, CSNet generates the feature by binary hashing and block-wise histograms. Finally, a linear SVM classifier is used to classify these features. The experiments on the MNIST dataset indicate that higher classification accuracy can be obtained by this algorithm.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1409.7307>)

[PDF](<https://arxiv.org/pdf/1409.7307.pdf>)