REST: Robust and Efficient Neural Networks for Sleep Monitoring in the Wild
New approach for sleep monitoring.
Nowadays a lot of people suffer from sleep disorders thataffects their daily functioning, long-term health and longevity. Thelong-term effects of sleep deprivation and sleep disorders includean increased risk of hypertension, diabetes, obesity, depression, heart attack, and stroke. As a result sleep monitoring is a very important topic.
Currently automatical documentation of sleep stages isn't robust against noises (which can be introduced by electrical interferences (e.g., power-line) and user motions (e.g., muscle contraction, respiration)) and isn't computationaly efficient enough for fast calculations on user devices.
The authors offer the following improvenents:
- adversarial training and spectral regularization to improve robustness to noise
- sparsity regularization to improve energy and computational efficiency
Rest models achieves a macro-F1 score of 0.67 vs. 0.39 for the state-of-the-art model in the presence of Gaussian noise, with 19×parameter and 15×MFLOPS reduction.
The model is also deployed onto a Pixel 2 smartphone. It achieves 17x energy reduction and 9x faster inference compared to uncompressed models.
Paper: https://arxiv.org/abs/2001.11363
Code: https://github.com/duggalrahul/REST
#deeplearning #compression #adversarial #sleepstaging
New approach for sleep monitoring.
Nowadays a lot of people suffer from sleep disorders thataffects their daily functioning, long-term health and longevity. Thelong-term effects of sleep deprivation and sleep disorders includean increased risk of hypertension, diabetes, obesity, depression, heart attack, and stroke. As a result sleep monitoring is a very important topic.
Currently automatical documentation of sleep stages isn't robust against noises (which can be introduced by electrical interferences (e.g., power-line) and user motions (e.g., muscle contraction, respiration)) and isn't computationaly efficient enough for fast calculations on user devices.
The authors offer the following improvenents:
- adversarial training and spectral regularization to improve robustness to noise
- sparsity regularization to improve energy and computational efficiency
Rest models achieves a macro-F1 score of 0.67 vs. 0.39 for the state-of-the-art model in the presence of Gaussian noise, with 19×parameter and 15×MFLOPS reduction.
The model is also deployed onto a Pixel 2 smartphone. It achieves 17x energy reduction and 9x faster inference compared to uncompressed models.
Paper: https://arxiv.org/abs/2001.11363
Code: https://github.com/duggalrahul/REST
#deeplearning #compression #adversarial #sleepstaging
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