Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localization

Understand

The essence of unsupervised anomaly detection is to learn the compact distribution of normal samples and detect outliers as anomalies in testing.

• Meanwhile, the anomalies in real-world are usually subtle and fine-grained in a high-resolution image especially for industrial applications.
• Towards this end, we propose a novel framework for unsupervised anomaly detection and localization.
• Our method aims at learning dense and compact distribution from normal images with a coarse-to-fine alignment process.