Paper

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection

arXiv:2607.14534v1 Announce Type: new Abstract: Industrial anomaly detection aims to identify and localize defective regions without relying on exhaustive annotations of all possible defect types. Although recent unsupervised methods have achieved strong performance, most are primarily designed for single-class settings and often struggle in multi-class scenarios, where diverse normal patterns may lead to over-generalization and reduce the discriminative capability between normal and anomalous regions. In this paper, we propose SwinAD, a reconstruction-based framework for multi-class unsuperv…

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