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
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Mahalanobis PatchCore adds covariance-aware whitening and incremental streaming aggregation to PatchCore, preserving benchmark performance while cutting peak memory from 5.41 GB to 2.78 GB and raising mean industrial AUC from 0.981 to 0.986.
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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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Mahalanobis PatchCore: Covariance-Aware and Streaming-Compatible Industrial Anomaly Detection
Mahalanobis PatchCore adds covariance-aware whitening and incremental streaming aggregation to PatchCore, preserving benchmark performance while cutting peak memory from 5.41 GB to 2.78 GB and raising mean industrial AUC from 0.981 to 0.986.