Advancing Pre-trained Teacher: Towards Robust Feature Discrepancy for Anomaly Detection

arXiv:2405.02068v1 Announce Type: new
Abstract: With the wide application of knowledge distillation between an ImageNet pre-trained teacher model and a learnable student model, industrial anomaly detection has witnessed a significant achievement in the past few years. The success of knowledge distillation mainly relies on how to keep the feature discrepancy between the teacher and student model, in which it assumes that: (1) the teacher model can jointly represent two different distributions for the normal and abnormal patterns, while (2) the …

abstract achievement anomaly anomaly detection application arxiv cs.cv detection distillation feature imagenet industrial knowledge robust success type