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Unsupervised Anomaly Detection and Localization with Generative Adversarial Networks

arXiv 2409.03657 ↗First posted 5 Sep 2024cs.LG eess.SPcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 12 references that are in the map. First posted on arXiv 5 Sep 2024.

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References · 2

  1. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  2. 2022Machine Learning-based Anomaly Detection in Optical Fiber Monitoringcited by 7

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