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

Abstract: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 Multiresolution Contrastive Distillation (MCD), which does not require positive/negative pairs with an anchor but operates by pulling/pushing the distance between the teacher and student features. Furthermore, we propose the blending module that transforms and aggregate multi-channel information to the three-channel input layer of MCD. Our proposed method significantly outperforms competitive state-of-the-art methods in both AUROC and accuracy metrics on the collected Multi-illumination and Multi-focus display image dataset for Anomaly Detection (MMdAD).

Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)

arXiv:2609.05520 [cs.CV]

(or arXiv:2609.05520v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2609.05520 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.23919/MVA57639.2023.10215808 Focus to learn more DOI(s) linking to related resources

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