CSE: Surface Anomaly Detection with Contrastively Selected Embedding
<|reference_start|>CSE: Surface Anomaly Detection with Contrastively Selected Embedding: Detecting surface anomalies of industrial materials poses a significant challenge within a myriad of industrial manufacturing processes. In recent times, various methodologies have emerged, capitalizing on the advantages of employing a network pre-trained on natural images for the extraction of representative features. Subsequently, these features are subjected to processing through a diverse range of techniques including memory banks, normalizing flow, and knowledge distillation, which have exhibited exceptional accuracy. This paper revisits approaches based on pre-trained features by introducing a novel method centered on target-specific embedding. To capture the most representative features of the texture under consideration, we employ a variant of a contrastive training procedure that incorporates both artificially generated defective samples and anomaly-free samples during training. Exploiting the intrinsic properties of surfaces, we derived a meaningful representation from the defect-free samples during training, facilitating a straightforward yet effective calculation of anomaly scores. The experiments conducted on the MVTEC AD and TILDA datasets demonstrate the competitiveness of our approach compared to state-of-the-art methods.<|reference_end|>
@article{thomine2024cse:,
    title={CSE: Surface Anomaly Detection with Contrastively Selected Embedding},
    author={Simon Thomine and Hichem Snoussi},
    journal={arXiv preprint arXiv:2403.01859},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.01859},
    primaryClass={cs.CV}
}