SOURCE-LINKED INTELLIGENCE

Contrastive Knowledge Distillation for Anomaly Detection in Multi-Illumination/Focus Display Images

arXiv · AI, language, vision and robotics · article · Sep 1, 2026 · UTC

In this paper, we tackle automatic anomaly detection in multi-illumination and multi-focus display images. The minute defects on the display surface are hard to spot out in RGB images and by a model trained with only normal data. To address this, we propose a novel contrastive learning scheme for knowledge distillation-based anomaly detection. In our framework, Multiresolution Knowledge Distillation (MKD) is adopted as a baseline, which operates by measuring feature similarities between the teacher and student networks. Based on MKD, we propose a novel contrastive learning method, namely Multi

Read original source ↗ Open in workspace

recordType

paper

region

Global