Compressed Learning: A Deep Neural Network Approach
Abstract
Compressed Learning (CL) is a joint signal processing and machine learning framework for inference from a signal, using a small number of measurements obtained by linear projections of the signal. In this paper we present an end-to-end deep learning approach for CL, in which a network composed of fully-connected layers followed by convolutional layers perform the linear sensing and non-linear inference stages. During the training phase, the sensing matrix and the non-linear inference operator are jointly optimized, and the proposed approach outperforms state-of-the-art for the task of image classification. For example, at a sensing rate of 1% (only 8 measurements of 28 X 28 pixels images), the classification error for the MNIST handwritten digits dataset is 6.46% compared to 41.06% with state-of-the-art.
Cite
@article{arxiv.1610.09615,
title = {Compressed Learning: A Deep Neural Network Approach},
author = {Amir Adler and Michael Elad and Michael Zibulevsky},
journal= {arXiv preprint arXiv:1610.09615},
year = {2016}
}