SPG uses sparse autoencoders to learn guide coefficients that generate normal and anomalous reference vectors, achieving competitive zero-shot anomaly detection and strong segmentation on MVTec AD and VisA without target adaptation.
Uninet: A con- trastive learning-guided unified framework with feature se- lection for anomaly detection
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SPG: Sparse-Projected Guides with Sparse Autoencoders for Zero-Shot Anomaly Detection
SPG uses sparse autoencoders to learn guide coefficients that generate normal and anomalous reference vectors, achieving competitive zero-shot anomaly detection and strong segmentation on MVTec AD and VisA without target adaptation.