Anomaly detection with Wasserstein GAN
Abstract
Generative adversarial networks are a class of generative algorithms that have been widely used to produce state-of-the-art samples. In this paper, we investigate GAN to perform anomaly detection on time series dataset. In order to achieve this goal, a bibliography is made focusing on theoretical properties of GAN and GAN used for anomaly detection. A Wasserstein GAN has been chosen to learn the representation of normal data distribution and a stacked encoder with the generator performs the anomaly detection. W-GAN with encoder seems to produce state of the art anomaly detection scores on MNIST dataset and we investigate its usage on multi-variate time series.
Keywords
Cite
@article{arxiv.1812.02463,
title = {Anomaly detection with Wasserstein GAN},
author = {Ilyass Haloui and Jayant Sen Gupta and Vincent Feuillard},
journal= {arXiv preprint arXiv:1812.02463},
year = {2018}
}
Comments
Based on Ilyass Haloui internship report. arXiv admin note: text overlap with arXiv:1701.07875, arXiv:1406.2661 by other authors