# Unsupervised Anomaly Detection and Localization with Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2409.03657 · HTML: https://mlsanity.com/paper/2409.03657/

## 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.

## Builds on (most cited first, 2 of 2)

- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [Machine Learning-based Anomaly Detection in Optical Fiber Monitoring](https://mlsanity.com/paper/2204.07059/) (2022)

## 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.