基于期望异常后验的辅助异常不确定性感知评估
Uncertainty-aware Evaluation of Auxiliary Anomalies with the Expected Anomaly Posterior
AI总结：
针对辅助合成异常质量评估缺失问题，提出基于不确定性的期望异常后验（EAP）评分函数，在40个基准数据集上优于12种现有估计器。
摘要：
异常检测是识别行为不符合预期样本的任务。由于异常是罕见且意外的事件，在多个应用中收集真实异常样本往往具有挑战性。此外，使用有限（或没有）异常样本学习异常检测器通常会导致预测性能不佳。一种选择是采用辅助合成异常来改进模型训练。然而，合成异常可能质量较差：不真实或与正常样本难以区分的异常可能会降低检测器的性能。不幸的是，现有方法均未量化辅助异常的质量。我们填补了这一空白，并提出了期望异常后验（EAP），一种基于不确定性的评分函数，通过量化异常检测器的总不确定性来衡量辅助异常的质量。在40个图像和表格数据基准数据集上的实验表明，在大多数情况下，EAP优于12种自适应的数据质量估计器。
英文摘要：
Anomaly detection is the task of identifying examples that do not behave as expected. Because anomalies are rare and unexpected events, collecting real anomalous examples is often challenging in several applications. In addition, learning an anomaly detector with limited (or no) anomalies often yields poor prediction performance. One option is to employ auxiliary synthetic anomalies to improve the model training. However, synthetic anomalies may be of poor quality: anomalies that are unrealistic or indistinguishable from normal samples may deteriorate the detector's performance. Unfortunately, no existing methods quantify the quality of auxiliary anomalies. We fill in this gap and propose the expected anomaly posterior (EAP), an uncertainty-based score function that measures the quality of auxiliary anomalies by quantifying the total uncertainty of an anomaly detector. Experimentally on 40 benchmark datasets of images and tabular data, we show that EAP outperforms 12 adapted data quality estimators in the majority of cases.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
