论文标题
OneFlow：基于最小体积区域的异常检测的一级流量
OneFlow: One-class flow for anomaly detection based on a minimal volume region
论文作者
论文摘要
我们提出了一个基于流动的单级分类器，用于异常（异常）检测，发现最小的体积边界区域。与基于密度的方法相反，OneFlow的构建方式通常不取决于离群值的结构。这是由于在训练期间仅在靠近决策边界（行为类似于SVM中的支持向量）的点上传播成本函数的梯度。流量模型和伯恩斯坦分位数估计器的组合允许OneFlow找到边界区域的参数形式，在各种应用中，这些形式在各种应用中都可以用，包括描述来自3D点云的形状。实验表明，所提出的模型优于现实世界异常检测问题的相关方法。
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.
