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arXiv Machine Learning By Jeongsu Park, Taehyeon Yun, Geonwoo Kim, Yuji Lim, Moohong Min

Don't Just Impute, Detect: Task-Aware Diffusion Pipelines for Edge Anomaly Detection

Read the original on arXiv Machine Learning →

The paper introduces Task‑Aware Diffusion Refiner (TADR), a conditional diffusion model that refines imputed data for edge anomaly detection in IIoT pipelines, addressing the bias of standard MSE‑trained imputers. TADR is trained against a frozen downstream detector’s loss landscape, requiring no extra back‑propagation through sampling, and achieves a 0.338 F1 improvement over the unrefined detector on the SMD dataset. The method’s effectiveness depends on the presence of continuous, redundant sensor channels and is less beneficial for telemetry data dominated by one‑hot command signals.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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