An Encoding Adversarial Network for Anomaly Detection

Elies Gherbi, Blaise Hanczar, Jean-Christophe Janodet, Witold Klaudel
; Proceedings of The Eleventh Asian Conference on Machine Learning, PMLR 101:188-203, 2019.

Abstract

Anomaly detection is a standard problem in Machine Learning with various applications such as health-care, predictive maintenance, and cyber-security. In such applications, the data is unbalanced: the rate of regular examples is much higher than the anomalous examples. The emergence of the Generative Adversarial Networks (GANs) has recently brought new algorithms for anomaly detection. Most of them use the generator as a proxy for the reconstruction loss. The idea is that the generator cannot reconstruct an anomaly. We develop an alternative approach for anomaly detection, based on an Encoding Adversarial Network (AnoEAN), which maps the data to a latent space (decision space), where the detection of anomalies is done directly by calculating a score. Our encoder is learned by adversarial learning, using two loss functions, the first constraining the encoder to project regular data into a Gaussian distribution and the second, to project anomalous data outside this distribution. We conduct a series of experiments on several standard bases and show that our approach outperforms the state of the art when using 10% anomalies during the learning stage, and detects unseen anomalies.

Cite this Paper


BibTeX
@InProceedings{pmlr-v101-gherbi19a, title = {An Encoding Adversarial Network for Anomaly Detection}, author = {Gherbi, Elies and Hanczar, Blaise and Janodet, Jean-Christophe and Klaudel, Witold}, pages = {188--203}, year = {2019}, editor = {Wee Sun Lee and Taiji Suzuki}, volume = {101}, series = {Proceedings of Machine Learning Research}, address = {Nagoya, Japan}, month = {17--19 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v101/gherbi19a/gherbi19a.pdf}, url = {http://proceedings.mlr.press/v101/gherbi19a.html}, abstract = {Anomaly detection is a standard problem in Machine Learning with various applications such as health-care, predictive maintenance, and cyber-security. In such applications, the data is unbalanced: the rate of regular examples is much higher than the anomalous examples. The emergence of the Generative Adversarial Networks (GANs) has recently brought new algorithms for anomaly detection. Most of them use the generator as a proxy for the reconstruction loss. The idea is that the generator cannot reconstruct an anomaly. We develop an alternative approach for anomaly detection, based on an Encoding Adversarial Network (AnoEAN), which maps the data to a latent space (decision space), where the detection of anomalies is done directly by calculating a score. Our encoder is learned by adversarial learning, using two loss functions, the first constraining the encoder to project regular data into a Gaussian distribution and the second, to project anomalous data outside this distribution. We conduct a series of experiments on several standard bases and show that our approach outperforms the state of the art when using 10% anomalies during the learning stage, and detects unseen anomalies.} }
Endnote
%0 Conference Paper %T An Encoding Adversarial Network for Anomaly Detection %A Elies Gherbi %A Blaise Hanczar %A Jean-Christophe Janodet %A Witold Klaudel %B Proceedings of The Eleventh Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2019 %E Wee Sun Lee %E Taiji Suzuki %F pmlr-v101-gherbi19a %I PMLR %J Proceedings of Machine Learning Research %P 188--203 %U http://proceedings.mlr.press %V 101 %W PMLR %X Anomaly detection is a standard problem in Machine Learning with various applications such as health-care, predictive maintenance, and cyber-security. In such applications, the data is unbalanced: the rate of regular examples is much higher than the anomalous examples. The emergence of the Generative Adversarial Networks (GANs) has recently brought new algorithms for anomaly detection. Most of them use the generator as a proxy for the reconstruction loss. The idea is that the generator cannot reconstruct an anomaly. We develop an alternative approach for anomaly detection, based on an Encoding Adversarial Network (AnoEAN), which maps the data to a latent space (decision space), where the detection of anomalies is done directly by calculating a score. Our encoder is learned by adversarial learning, using two loss functions, the first constraining the encoder to project regular data into a Gaussian distribution and the second, to project anomalous data outside this distribution. We conduct a series of experiments on several standard bases and show that our approach outperforms the state of the art when using 10% anomalies during the learning stage, and detects unseen anomalies.
APA
Gherbi, E., Hanczar, B., Janodet, J. & Klaudel, W.. (2019). An Encoding Adversarial Network for Anomaly Detection. Proceedings of The Eleventh Asian Conference on Machine Learning, in PMLR 101:188-203

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