论文标题
休息：野外睡眠监测的稳健有效的神经网络
REST: Robust and Efficient Neural Networks for Sleep Monitoring in the Wild
论文作者
论文摘要
近年来，已大大关注将深度学习技术纳入医疗领域。但是，为了安全，实际上为家庭健康监测部署深度学习模型，必须解决两个重大挑战：模型应（1）与噪声的强大； （2）紧凑而节能。我们提出了REST，这是一种新方法，通过1）对抗性训练并通过光谱正则化来控制神经网络的Lipschitz常数，而2）通过稀疏正规化实现神经网络压缩。我们证明，静止产生高度和高效的模型，在存在噪声的情况下，基本上优于原始的全尺寸模型。对于单渠道脑电图（EEG）的睡眠分期任务，其余模型在存在19倍参数减少的同时，在存在19倍参数减少的同时，通过最先进的模型实现了0.67 vs. 0.67 vs. 0.39，而在两个大型现实的，现实的EEG EEG数据降低了15倍Mflops。通过将这些模型部署到智能手机上的Android应用程序中，我们定量地观察到REST允许模型可实现高达17倍的能量降低和更快的推理。我们使用本文开放代码存储库：https：//github.com/duggalahul/rest。
In recent years, significant attention has been devoted towards integrating deep learning technologies in the healthcare domain. However, to safely and practically deploy deep learning models for home health monitoring, two significant challenges must be addressed: the models should be (1) robust against noise; and (2) compact and energy-efficient. We propose REST, a new method that simultaneously tackles both issues via 1) adversarial training and controlling the Lipschitz constant of the neural network through spectral regularization while 2) enabling neural network compression through sparsity regularization. We demonstrate that REST produces highly-robust and efficient models that substantially outperform the original full-sized models in the presence of noise. For the sleep staging task over single-channel electroencephalogram (EEG), the REST model achieves a macro-F1 score of 0.67 vs. 0.39 achieved by a state-of-the-art model in the presence of Gaussian noise while obtaining 19x parameter reduction and 15x MFLOPS reduction on two large, real-world EEG datasets. By deploying these models to an Android application on a smartphone, we quantitatively observe that REST allows models to achieve up to 17x energy reduction and 9x faster inference. We open-source the code repository with this paper: https://github.com/duggalrahul/REST.
