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Normalizing flows for deep anomaly detection

12/19/2019

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by Artem Ryzhikov, et al.

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Higher School of Economics

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Anomaly detection for complex data is a challenging task from the perspective of machine learning. In this work, weconsider cases with missing certain kinds of anomalies in the training dataset, while significant statistics for the normal class isavailable. For such scenarios, conventional supervised methods might suffer from the class imbalance, while unsupervised methodstend to ignore difficult anomalous examples. We extend the idea of the supervised classification approach for class-imbalanceddatasets by exploiting normalizing flows for proper Bayesian inference of the posterior probabilities.

Artem Ryzhikov
5 publications
Maxim Borisyak
9 publications
Andrey Ustyuzhanin
20 publications
Denis Derkach
15 publications

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