利用神经常微分方程提高频谱图分类器的鲁棒性
Improving Robustness of Spectrogram Classifiers with Neural Stochastic Differential Equations
AI总结：
本文提出利用神经常微分方程增强频谱图分类器在低信噪比和动态噪声环境下的鲁棒性，以解决智能电网感知、异常检测等关键基础设施领域现有视觉模型不适配的问题。
摘要：
信号分析与分类充斥着高水平的噪声和扰动。应用于频谱图的基于计算机视觉的深度学习模型已被证明在信号分类和检测领域是有用的；然而，这些方法并非为处理非视觉信号处理任务中固有的低信噪比而设计。尽管它们功能强大，但在本质上具有噪声和动态特性的关键基础设施领域，如智能电网感知、异常检测和非侵入式负荷监测中，它们目前并不是首选方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
