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arXiv · 2409.03657

Unsupervised Anomaly Detection and Localization with Generative Adversarial Networks

Khouloud Abdelli·Matteo Lonardi·Jurgen Gripp·Samuel Olsson·Fabien Boitier·Patricia Layec
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Abstract

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.

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Khouloud Abdelli, Matteo Lonardi, Jurgen Gripp, Samuel Olsson, Fabien Boitier, Patricia Layec. 2024-09-05. Unsupervised Anomaly Detection and Localization with Generative Adversarial Networks. https://arxiv.org/abs/2409.03657

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