SOURCE-LINKED INTELLIGENCE

Interpretable Multi-Hypersphere Deep Anomaly Detection for Open-set Supervised Anomaly Detection

arXiv · AI, language, vision and robotics · article · Sep 19, 2026 · UTC

Multi-class open-set anomaly detection requires a model to characterize the normal acceptance domain formed by multiple heterogeneous subdistributions using only class-labeled samples from known normal classes, and to identify previously unseen anomalies at test time. Existing single-hypersphere methods cannot explicitly represent class-specific locations and acceptance ranges, while current multi-hypersphere or multi-class approaches do not fully integrate inter-class boundary constraints, learnable acceptance ranges, and interpretable decisions. To address these limitations, we propose Inter

Read original source ↗ Open in workspace

recordType

paper

region

Global