From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection

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arXiv:2608.07770v1 Announce Type: new Abstract: Automated anomaly detection methods often report strong performance on curated academic benchmarks, but their behavior under real-world industrial conditions is less clear. In this work, we evaluate 19 unsupervised anomaly detection models on the BowTie dataset, a challe…