Improving Robustness of Spectrogram Classifiers with Neural Stochastic Differential Equations
<|reference_start|>Improving Robustness of Spectrogram Classifiers with Neural Stochastic Differential Equations: 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.<|reference_end|>
@article{brogan2024improving,
    title={Improving Robustness of Spectrogram Classifiers with Neural Stochastic
  Differential Equations},
    author={Joel Brogan, Olivera Kotevska, Anibely Torres, Sumit Jha, Mark Adams},
    journal={arXiv preprint arXiv:2409.01532},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.01532},
    primaryClass={cs.LG cs.AI cs.CV}
}