论文标题
具有不确定性量化的贝叶斯自动编码器：迈向值得信赖的异常检测
Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection
论文作者
论文摘要
尽管针对无监督的异常检测进行了大量研究，但AES仍然缺乏表达预测不确定性的方法，对于确保在高速应用中确保安全且值得信赖的机器学习系统至关重要。因此，在这项工作中，采用了贝叶斯自动编码器（BAE）的制定，以量化总体异常不确定性，包括认知和核心不确定性。为了评估不确定性的质量，我们考虑将异常分类的任务与拒绝高不确定性预测的其他选择。此外，我们使用准确性拒绝曲线，并提出加权平均精度作为性能度量。我们的实验证明了BAE和一组基准数据集和两个用于制造的真实数据集的有效性：一个用于状态监测，另一个用于质量检查。
Despite numerous studies of deep autoencoders (AEs) for unsupervised anomaly detection, AEs still lack a way to express uncertainty in their predictions, crucial for ensuring safe and trustworthy machine learning systems in high-stake applications. Therefore, in this work, the formulation of Bayesian autoencoders (BAEs) is adopted to quantify the total anomaly uncertainty, comprising epistemic and aleatoric uncertainties. To evaluate the quality of uncertainty, we consider the task of classifying anomalies with the additional option of rejecting predictions of high uncertainty. In addition, we use the accuracy-rejection curve and propose the weighted average accuracy as a performance metric. Our experiments demonstrate the effectiveness of the BAE and total anomaly uncertainty on a set of benchmark datasets and two real datasets for manufacturing: one for condition monitoring, the other for quality inspection.
