Explainable Deep One-Class Classification

Philipp Liznerski
Lukas Ruff
Robert Vandermeulen
Billy Joe Franks
Marius Kloft
ICLR 2021(2021) (to appear)


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