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Found 3 papers, 1 papers with code
AUPIMO: Redefining Visual Anomaly Detection Benchmarks with High Speed and Low Tolerance
Recent advances in visual anomaly detection research have seen AUROC and AUPRO scores on public benchmark datasets such as MVTec and VisA converge towards perfect recall, giving the impression that these benchmarks are near-solved.
Adapting the Hypersphere Loss Function from Anomaly Detection to Anomaly Segmentation
We propose an incremental improvement to Fully Convolutional Data Description (FCDD), an adaptation of the one-class classification approach from anomaly detection to image anomaly segmentation (a. k. a.
[Reproducibility Report] Explainable Deep One-Class Classification
Fully Convolutional Data Description (FCDD), an explainable version of the Hypersphere Classifier (HSC), directly addresses image anomaly detection (AD) and pixel-wise AD without any post-hoc explainer methods.
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