Anomaly Multi-classification in Industrial Scenarios: Transferring Few-shot Learning to a New Task
This addresses the need for detailed anomaly classification in industrial quality control, but it is incremental as it adapts existing few-shot learning methods to a new task.
The paper tackles the problem of classifying multiple types of anomalies in industrial settings, where data is limited, by proposing a baseline model combining RelationNet and PatchCore with data generation and contrastive learning, achieving superior performance on MvTec AD and MvTec3D AD datasets.
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.