Unsupervised Anomaly Detection and Localization with Generative Adversarial Networks
arXiv:2409.03657v13 citationsh-index: 17
Originality Incremental advance
AI Analysis
This addresses anomaly detection in fiber optic networks without requiring labeled data, which is incremental as it applies existing GAN methods to a new domain.
The paper tackled unsupervised anomaly detection and localization using generative adversarial networks and SOP-derived spectrograms, achieving over 97% accuracy on SOP datasets from submarine and terrestrial fiber links without labeled data.
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.