Deep one-class classification variants for anomaly detection learn a mapping that concentrates nominal samples in feature space causing anomalies to be mapped away. Because this transformation is highly non-linear, finding interpretations poses a significant challenge. In this paper we present an explainable deep one-class classification method, Fully Convolutional Data Description (FCDD), where the mapped samples are themselves also an explanation heatmap. FCDD yields competitive detection performance and provides reasonable explanations on common anomaly detection benchmarks with CIFAR-10 and ImageNet. On MVTec-AD, a recent manufacturing dataset offering ground-truth anomaly maps, FCDD meets the state of the art in an unsupervised setting, and outperforms its competitors in a semi-supervised setting. Finally, using FCDD's explanations we demonstrate the vulnerability of deep one-class classification models to spurious image features such as image watermarks.

本文提出一种可解释的深度单类分类方法 FCDD，它基于卷积神经网络，通过非线性映射，将异常数据映射到远离正常数据的地方，从而检测异常。FCDD 在常见异常检测基准测试中取得了竞争性能，还能够提供合理的异常检测解释。此外，使用 FCDD 的解释，作者还展示了深度单类分类模型对于图片水印这类假象特征的脆弱性。