Enhancing Industrial Anomaly Detection with RealNet: A Unified AI Framework for Realistic Anomaly Synthesis and Efficient Feature Reconstruction

In industrial image anomaly detection, self-supervised feature reconstruction methods show promise but still grapple with challenges such as generating realistic and diverse anomaly samples while mitigating feature redundancy and pre-training bias. Synthetic anomalies lack diversity and realism, hindering model generalization. Meanwhile, feature reconstruction-based detection, though simple, needs to improve with high computational demands and requires […]

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