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arXiv Computer Vision By Huan Wang, Jun Shen, Jun Yan, Guansong Pang

Beyond Normal References: Discriminative Few-Shot Anomaly Detection

Read the original on arXiv Computer Vision →

This paper introduces IDEAL, a framework for few-shot anomaly detection that uses both normal and anomalous reference examples. IDEAL suppresses irrelevant normal variations and encodes intrinsic deviation vectors that capture discriminative anomaly directions. Experiments on eight real-world datasets show that IDEAL generalizes to unseen anomalies and outperforms existing methods.

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