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.

中文翻译：

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OneFlow：基于最小体积区域的异常检测的一类流。

我们提出了 OneFlow——一种基于流的一类分类器，用于异常（离群值）检测，可找到最小体积边界区域。与基于密度的方法相反，OneFlow 的构造方式使其结果通常不依赖于异常值的结构。这是因为在训练过程中，成本函数的梯度仅在靠近决策边界的点上传播（行为类似于 SVM 中的支持向量）。流模型和 Bernstein 分位数估计器的组合允许 OneFlow 找到边界区域的参数形式，这在包括描述 3D 点云形状在内的各种应用中都很有用。实验表明，所提出的模型在实际异常检测问题上优于相关方法。