Explainable Deep One-Class Classification

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 sets a
new state of the art in the unsupervised setting. Our method can incorporate groundtruth anomaly maps during training and using even a few of these (∼ 5) improves
performance significantly. Finally, using FCDD’s explanations we demonstrate the
vulnerability of deep one-class classification models to spurious image features
such as image watermarks

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