Compressed Classification from Learned Measurements
Compressed Classification from Learned Measurements
复制标题
DOI：
10.1109/iccvw54120.2021.00449
复制
发表时间：
2021-10
期刊：
影响因子：
--
通讯作者：
Robiulhossain Mdrafi;A. Gürbüz
中科院分区：
文献类型：
--
作者：
Robiulhossain Mdrafi;A. Gürbüz
这项工作提出了一个深度压缩学习框架，直接从压缩测量推断分类。经典方法分别感知、重构信号，并对这些重构进行分类，而我们利用一种具有新型损失函数的深度神经网络，共同学习感知和分类方案。我们的方法在压缩学习框架内采用数据驱动的重建网络，利用结合了网络内重建和分类损失的加权损失。该网络结构还学习了最优度量矩阵，以提高分类性能。在CIFAR-10图像数据集上的定量结果表明，与经过测试的最先进的深度压缩学习方法相比，所提出的框架具有更好的分类性能和对噪声的鲁棒性。
This work proposes a deep compressed learning framework inferring classification directly from the compressive measurements. While classical approaches separately sense, reconstruct signals, and apply classification on these reconstructions, we jointly learn the sensing and classification schemes utilizing a deep neural network with a novel loss function. Our approach employs a data-driven reconstruction network within the compressed learning framework utilizing a weighted loss that combines both in-network reconstruction and classification losses. The proposed network structure also learns the optimal measurement matrices for the goal of enhancing classification performance. Quantitative results demonstrated on CIFAR-10 image dataset show that the proposed framework provides better classification performance and robustness to noise compared to the tested state of the art deep compressed learning approaches.