Summary of Unsupervised Anomaly Detection and Localization with Generative Adversarial Networks, by Khouloud Abdelli et al.
Unsupervised Anomaly Detection and Localization with Generative Adversarial Networks
by Khouloud Abdelli, Matteo Lonardi, Jurgen Gripp, Samuel Olsson, Fabien Boitier, Patricia Layec
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel unsupervised anomaly detection approach is proposed that leverages generative adversarial networks (GANs) and spectrogram-based (SOP) derived spectrograms. This method demonstrates remarkable efficacy, achieving over 97% accuracy on SOP datasets from both submarine and terrestrial fiber links without requiring labelled data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Our method uses GANs to develop a model that can detect anomalies in SOP-derived spectrograms. This approach is effective for detecting anomalies in optical fiber communications, which is important for maintaining the reliability of these networks. |
Keywords
» Artificial intelligence » Anomaly detection » Unsupervised
