Asymmetric Student-Teacher Networks for Industrial Anomaly Detection

Understand

Industrial defect detection is commonly addressed with anomaly detection (AD) methods where no or only incomplete data of potentially occurring defects is available.

• This work discovers previously unknown problems of student-teacher approaches for AD and proposes a solution, where two neural networks are trained to produce the same output for the defect-free training examples.
• The core assumption of student-teacher networks is that the distance between the outputs of both networks is larger for anomalies since they are absent in training.
• However, previous methods suffer from the similarity of student and teacher architecture, such that the distance is undesirably small for anomalies.