Reconstructed Student-Teacher and Discriminative Networks for Anomaly Detection

Understand

Anomaly detection is an important problem in computer vision; however, the scarcity of anomalous samples makes this task difficult.

• Thus, recent anomaly detection methods have used only normal images with no abnormal areas for training.
• In this work, a powerful anomaly detection method is proposed based on student-teacher feature pyramid matching (STPM), which consists of a student and teacher network.
• Generative models are another approach to anomaly detection.