Fig. 3
From: Hyperbolic geometry enhanced feature filtering network for industrial anomaly detection
The overall architecture of HADNet. The architecture consists of three main modules: Hyperbolic Space Transformation (HST), Anomaly-aware Feature Subset Selection (AFSS), and Adaptive Residuals Discrimination (ARD). The input features are first mapped from Euclidean space to hyperbolic space using HST to enhance the representation of hierarchical relationships. The AFSS module then selects the most relevant features for anomaly detection, reducing redundancy and improving detection accuracy. Finally, the ARD module analyzes the residuals to isolate the most effective regions for anomaly detection and generates anomaly scores.
