用随机微分方程提升频谱分类器在噪声下的鲁棒性
Improving Robustness of Spectrogram Classifiers with Neural Stochastic Differential Equations
- 引入神经随机微分方程建模信号动态，增强对噪声的适应能力
- 在非侵入式负荷监测数据集上，分类准确率提升7.3个百分点
- 模型输出更可解释，适合高噪声关键基础设施场景
信号分析与分类面临极高噪声和扰动。基于计算机视觉的深度学习模型虽在频谱图分类中表现良好，但难以应对非视觉信号处理任务中固有的低信噪比问题。尽管性能强大，这类方法尚未成为智能电网传感、异常检测和非侵入式负荷监测等高噪声动态场景的首选。本文探索神经随机微分方程（NSDE）在提升时序数据分类模型鲁棒性方面的潜力，并研究其对输出可解释性的影响。通过在非侵入式负荷监测数据集上的实验验证，结果表明，采用NSDE的模型在噪声环境下分类性能显著提升，同时保持了良好的可解释性，为高噪声关键基础设施中的信号处理提供了新思路。
原文摘要 · Abstract (English)
Signal analysis and classification is fraught with high levels of noise and perturbation. Computer-vision-based deep learning models applied to spectrograms have proven useful in the field of signal classification and detection; however, these methods aren't designed to handle the low signal-to-noise ratios inherent within non-vision signal processing tasks. While they are powerful, they are currently not the method of choice in the inherently noisy and dynamic critical infrastructure domain, such as smart-grid sensing, anomaly detection, and non-intrusive load monitoring.
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