Attention-Guided Perturbation Network for Industrial Anomaly Detection
In unsupervised image anomaly detection, reconstruction-based methods learn normal patterns for data reconstruction, but often undesirably reconstruct anomalous regions at inference, resulting in missed detections. To alleviate this, existing approaches perturb normal samples in a sample-agnostic manner by uniformly injecting noise, ignoring that foreground regions are more critical for robust reconstruction. To address this limitation, we propose Attention-Guided Perturbation Network (AGPNet), a novel reconstruction framework for industrial anomaly detection. AGPNet introduces sample-aware attention masks to guide perturbations, enhancing the learning of invariant normal patterns at important locations. AGPNet consists of two branches, a reconstruction branch and an auxiliary attention-based perturbation one. The reconstruction branch focuses on learning to reconstruct normal samples, while the auxiliary branch generates attention masks to guide noise perturbation. By applying stronger perturbations to salient regions, the reconstruction branch learns intrinsic normal patterns in a more comprehensive and robust manner. Extensive experiments on MVTec-AD, VisA, and MVTec-3D show that AGPNet achieves competitive or leading performance across few-shot, one-class, and multi-class settings, with particularly strong performance in few-shot anomaly localization.