  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.
