Novel Network for Few-Shot Anomaly Detection in Images

This research paper proposes a novel approach to few-shot anomaly detection leveraging prototype learning and context-aware segmentation. The focus on few-shot learning is a significant area of research given the limited labeled data in anomaly detection scenarios.

• •Addresses few-shot anomaly detection, a practical challenge.
• •Employs a combination of prototype learning and context-aware segmentation.
• •Published on ArXiv, suggesting early stage research.