[Reproducibility Report] Explainable Deep One-Class Classification

Scope of Reproducibility Liznerski et al. [23] proposed Fully Convolutional Data Description (FCDD), an explainable version of the Hypersphere Classifier (HSC) to directly address image anomaly detection (AD) and pixel-wise AD without any post-hoc explainer methods. The authors claim that FCDD achieves results comparable with the state-of-the-art in sample-wise AD on Fashion-MNIST and CIFAR-10 and exceeds the state-of-the-art on the pixel-wise task on MVTec-AD. They also give evidence to show a clear improvement by using few (1 up to 8) real anomalous images in MVTec-AD for supervision at the pixel level. Finally, a qualitative study with horse images on PASCAL-VOC shows that FCDD can intrinsically reveal spurious model decisions by providing built-in anomaly score heatmaps. Methodology We have reproduced the quantitative results in the main text of [23] except for the performance on ImageNet: samplewise AD on Fashion-MNIST and CIFAR-10, and pixel-wise AD on MVTec-AD.