Anomaly Multi-classification in Industrial Scenarios: Transferring Few-shot Learning to a New Task

• URL: http://arxiv.org/abs/2406.05645v1
• Date: Sun, 9 Jun 2024 05:07:39 GMT
• Title: Anomaly Multi-classification in Industrial Scenarios: Transferring Few-shot Learning to a New Task
• Authors: Jie Liu, Yao Wu, Xiaotong Luo, Zongze Wu,
• Abstract summary: This paper proposes a novel and valuable research task called anomaly multi-classification. Given the challenges in applying few-shot learning to this task, 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.
• Score: 14.949274810807477
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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