Introduces a unified benchmark for continual anomaly detection with discrete and continuous protocols plus a training-free DINOSaur method that outperforms prior CAD approaches with zero forgetting and sub-100ms edge inference.
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Pith papers citing it
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
TaskFusion combines AGF feature mapping, cross-task augmentation, and distilled replay for continual anomaly detection on heterogeneous tabular data, reporting gains over baselines on 21 datasets.
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Rethinking Continual Anomaly Detection on the Edge: Benchmarking Under Realistic Industrial Conditions
Introduces a unified benchmark for continual anomaly detection with discrete and continuous protocols plus a training-free DINOSaur method that outperforms prior CAD approaches with zero forgetting and sub-100ms edge inference.
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TaskFusion: Continual Anomaly Detection for Heterogeneous Tabular Data
TaskFusion combines AGF feature mapping, cross-task augmentation, and distilled replay for continual anomaly detection on heterogeneous tabular data, reporting gains over baselines on 21 datasets.