论文标题
$ \ text {a}^3 $：激活异常分析
$\text{A}^3$: Activation Anomaly Analysis
论文作者
论文摘要
受神经网络覆盖范围引导分析的最新进展的启发，我们提出了一种新型的异常检测方法。我们表明，隐藏的激活值包含有助于区分正常样本和异常样品的信息。我们的方法将三个神经网络结合在纯粹由数据驱动的端到端模型中。根据目标网络中的激活值，警报网络决定给定样本是否正常。多亏了异常网络，我们的方法甚至可以在严格的半监督设置中起作用。在超过当前基线方法的通用数据集上，实现了强大的异常检测结果。我们的半监督异常检测方法允许检查各种应用程序中异常的大量数据。
Inspired by recent advances in coverage-guided analysis of neural networks, we propose a novel anomaly detection method. We show that the hidden activation values contain information useful to distinguish between normal and anomalous samples. Our approach combines three neural networks in a purely data-driven end-to-end model. Based on the activation values in the target network, the alarm network decides if the given sample is normal. Thanks to the anomaly network, our method even works in strict semi-supervised settings. Strong anomaly detection results are achieved on common data sets surpassing current baseline methods. Our semi-supervised anomaly detection method allows to inspect large amounts of data for anomalies across various applications.
