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Papers/Explainable Deep One-Class Classification

Explainable Deep One-Class Classification

Philipp Liznerski, Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, Marius Kloft, Klaus-Robert Müller

2020-07-03ICLR 2021 1Outlier DetectionAnomaly DetectionGeneral ClassificationClassification
PaperPDFCode(official)

Abstract

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.

Results

TaskDatasetMetricValueModel
Anomaly DetectionOne-class ImageNet-30AUROC91FCDD
Anomaly DetectionOne-class CIFAR-10AUROC92FCDD
Anomaly DetectionMVTec ADSegmentation AUROC94FCDD (semi-supervised)
Anomaly DetectionMVTec ADSegmentation AUROC88FCDD (unsupervised)

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