Anomaly detection with Wasserstein GAN
| Autor: | Haloui, Ilyass, Gupta, Jayant Sen, Feuillard, Vincent |
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| Rok vydání: | 2018 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: Based on Ilyass Haloui internship report. arXiv admin note: text overlap with arXiv:1701.07875, arXiv:1406.2661 by other authors |
| Databáze: | arXiv |
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