Disentangled Knowledge Distillation for Unified Multi-Class Anomaly Detection

Anomaly detection is essential for image-based industrial inspection, yet class-specific models hinder its scalability and adaptability. This issue is exacerbated by ‘normality confusion’, where models struggle to distinguish normal from abnormal features across diverse classes. To address this, we introduce TwoStudents, a unified anomaly detection framework utilizing a knowledge distillation model with dual student decoders for efficient feature disentanglement. By separating class-specific abnormal features from normal ones, TwoStudents strengthens the unified normal representation. Our approach employs image transformations to simulate anomalies and uses cross-distillation training techniques on features derived from normal images and their corresponding simulated abnormal images. This effectively isolates abnormal features, enabling a unified model applicable to all classes. Evaluations on the MVTec AD, VisA, and BTAD datasets demonstrate that TwoStudents outperforms existing methods, achieving state-of-the-art performance. Notably, it significantly improves fine-grained localization, as measured by pixel AUPRO, with increases from 91.9% to 94.0% on MVTec AD and 87.6% to 90.5% on VisA.