AdapTS: Lightweight Teacher-Student Approach for Multi-Class and Continual Visual Anomaly Detection

ArXi:2603.17530v1 Announce Type: new Visual Anomaly Detection (VAD) is crucial for industrial inspection, yet most existing methods are limited to single-category scenarios, failing to address the multi-class and continual learning demands of real-world environments. While Teacher-Student (TS) architectures are efficient, they remain unexplored for the Continual Setting. To bridge this gap, we propose AdapTS, a unified TS framework designed for multi-class and continual settings, optimized for edge deployment.