A new multimodal anomaly detection benchmark for battery electrode manufacturing shows that existing methods have high false-positive rates, and a density-aware coreset method modestly improves FPR95.
InProceedings of the IEEE/CVFConferenceonComputerVisionandPattern Recognition, 17234–17243
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LIBAD: A Multimodal Anomaly Detection Benchmark for Li-Ion Battery Electrode Manufacturing
A new multimodal anomaly detection benchmark for battery electrode manufacturing shows that existing methods have high false-positive rates, and a density-aware coreset method modestly improves FPR95.