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weakly supervised anomaly detection
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weakly-supervised-anomaly-detection-62be6dde·1 events·first seen 22d agoAliases: weakly supervised anomaly detection
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time-series anomaly detectionout-of-distribution detectionSelf-Supervised Learningimportance-weighted supervised fine-tuninganomalycoSupervised Semantic Differentialstance detectionSafety Detection Classifiersupervised fine-tuningunsupervised learningSoft Label Supervisionerror-aware specialization objective
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WSADBench: A Unified Benchmark for Weakly Supervised Anomaly Detection
WSADBench is the first benchmark to unify evaluation across the three primary weakly supervised anomaly detection (WSAD) paradigms—incomplete, inexact, and inaccurate supervision—testing 36 algorithms across 4 modalities with over 700K experiments. Key findings challenge the isolation of current WSAD research directions, showing strong correlations between supervision scenarios and that specialized WSAD methods are quickly outperformed by tabular foundation models as label availability increases. The benchmark also reveals inconsistent utility of unlabeled data and asymmetric model sensitivity to label noise types. Code and datasets are released open-source.