2019-10-15-gherbi19a.md

title	crossref	abstract	layout	series	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	publisher	container-title	volume	genre	issued	pdf	extras
An Encoding Adversarial Network for Anomaly Detection	acml19	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.	inproceedings	Proceedings of Machine Learning Research	gherbi19a	0	An Encoding Adversarial Network for Anomaly Detection	188	203	188-203	188	false	Gherbi, Elies and Hanczar, Blaise and Janodet, Jean-Christophe and Klaudel, Witold	given family Elies Gherbi given family Blaise Hanczar given family Jean-Christophe Janodet given family Witold Klaudel	2019-10-15		PMLR	Proceedings of The Eleventh Asian Conference on Machine Learning	101	inproceedings	date-parts 2019 10 15	http://proceedings.mlr.press/v101/gherbi19a/gherbi19a.pdf