COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection

Understand

Existing approaches towards anomaly detection~(AD) often rely on a substantial amount of anomaly-free data to train representation and density models.

• However, large anomaly-free datasets may not always be available before the inference stage; in which case an anomaly detection model must be trained with only a handful of normal samples, a.k.a.
• few-shot anomaly detection (FSAD).
• In this paper, we propose a novel methodology to address the challenge of FSAD which incorporates two important techniques.