Contrastive Predictive Coding for Anomaly Detection
This work addresses the problem of reliable anomaly detection and segmentation for machine learning practitioners, but it is incremental as it adapts an existing method to a new task.
The paper tackled the challenge of anomaly detection and segmentation by using Contrastive Predictive Coding to directly interpret its patch-wise contrastive loss as an anomaly score, achieving promising results on the MVTec-AD dataset.
Reliable detection of anomalies is crucial when deploying machine learning models in practice, but remains challenging due to the lack of labeled data. To tackle this challenge, contrastive learning approaches are becoming increasingly popular, given the impressive results they have achieved in self-supervised representation learning settings. However, while most existing contrastive anomaly detection and segmentation approaches have been applied to images, none of them can use the contrastive losses directly for both anomaly detection and segmentation. In this paper, we close this gap by making use of the Contrastive Predictive Coding model (arXiv:1807.03748). We show that its patch-wise contrastive loss can directly be interpreted as an anomaly score, and how this allows for the creation of anomaly segmentation masks. The resulting model achieves promising results for both anomaly detection and segmentation on the challenging MVTec-AD dataset.