  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 ground-truth 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.
