OneFlow: One-class flow for anomaly detection based on a minimal volume region

• URL: http://arxiv.org/abs/2010.03002v3
• Date: Wed, 22 Sep 2021 18:51:47 GMT
• Title: OneFlow: One-class flow for anomaly detection based on a minimal volume region
• Authors: {\L}ukasz Maziarka, Marek \'Smieja, Marcin Sendera, {\L}ukasz Struski, Jacek Tabor, Przemys{\l}aw Spurek
• Abstract summary: OneFlow is a flow-based one-class classifier for anomaly (outlier) detection. It is constructed in such a way that its result does not depend on the structure of outliers. The proposed model outperforms related methods on real-world anomaly detection problems.
• Score: 12.691473293758607
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose OneFlow - a flow-based one-class classifier for anomaly (outlier) detection that finds a minimal volume bounding region. Contrary to density-based methods, OneFlow is constructed in such a way that its result typically does not depend on the structure of outliers. This is caused by the fact that during training the gradient of the cost function is propagated only over the points located near to the decision boundary (behavior similar to the support vectors in SVM). The combination of flow models and a Bernstein quantile estimator allows OneFlow to find a parametric form of bounding region, which can be useful in various applications including describing shapes from 3D point clouds. Experiments show that the proposed model outperforms related methods on real-world anomaly detection problems.

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