Unsupervised Anomaly Detection and Localization with Generative Adversarial Networks
<|reference_start|>Unsupervised Anomaly Detection and Localization with Generative Adversarial Networks: We propose a novel unsupervised anomaly detection approach using generative adversarial networks and SOP-derived spectrograms. Demonstrating remarkable efficacy, our method achieves over 97% accuracy on SOP datasets from both submarine and terrestrial fiber links, all achieved without the need for labelled data.<|reference_end|>
@article{abdelli2024unsupervised,
    title={Unsupervised Anomaly Detection and Localization with Generative
  Adversarial Networks},
    author={Khouloud Abdelli, Matteo Lonardi, Jurgen Gripp, Samuel Olsson, Fabien
  Boitier, and Patricia Layec},
    journal={ECOC 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.03657},
    primaryClass={cs.LG eess.SP}
}