Anomaly Multi-classification in Industrial Scenarios: Transferring Few-shot Learning to a New Task
<|reference_start|>Anomaly Multi-classification in Industrial Scenarios: Transferring Few-shot Learning to a New Task: In industrial scenarios, it is crucial not only to identify anomalous items but also to classify the type of anomaly. However, research on anomaly multi-classification remains largely unexplored. This paper proposes a novel and valuable research task called anomaly multi-classification. Given the challenges in applying few-shot learning to this task, due to limited training data and unique characteristics of anomaly images, we introduce a baseline model that combines RelationNet and PatchCore. We propose a data generation method that creates pseudo classes and a corresponding proxy task, aiming to bridge the gap in transferring few-shot learning to industrial scenarios. Furthermore, we utilize contrastive learning to improve the vanilla baseline, achieving much better performance than directly fine-tune a ResNet. Experiments conducted on MvTec AD and MvTec3D AD demonstrate that our approach shows superior performance in this novel task.<|reference_end|>
@article{liu2024anomaly,
    title={Anomaly Multi-classification in Industrial Scenarios: Transferring
  Few-shot Learning to a New Task},
    author={Jie Liu, Yao Wu, Xiaotong Luo, Zongze Wu},
    journal={arXiv preprint arXiv:2406.05645},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.05645},
    primaryClass={cs.CV cs.AI cs.LG}
}