Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization

TL;DR

This paper introduces a novel unsupervised anomaly detection method that uses prototype learning with optimal transport to effectively capture the structure of normal data, improving detection and localization in industrial images.

Contribution

It proposes a new approach combining local and global prototypes with a structured metric based on optimal transport, enhancing anomaly detection without labels.

Findings

Achieves competitive performance on industrial image benchmarks.

Effectively captures normal data structure for better anomaly localization.

Utilizes a novel metric balancing feature and spatial costs.

Abstract

Unsupervised anomaly detection aims to detect defective parts of a sample by having access, during training, to a set of normal, i.e. defect-free, data. It has many applications in fields, such as industrial inspection or medical imaging, where acquiring labels is costly or when we want to avoid introducing biases in the type of anomalies that can be spotted. In this work, we propose a novel UAD method based on prototype learning and introduce a metric to compare a structured set of embeddings that balances a feature-based cost and a spatial-based cost. We leverage this metric to learn local and global prototypes with optimal transport from latent representations extracted with a pre-trained image encoder. We demonstrate that our approach can enforce a structural constraint when learning the prototypes, allowing to capture the underlying organization of the normal samples, thus improving the detection of incoherencies in images. Our model achieves performance that is on par with strong baselines on two reference benchmarks for anomaly detection on industrial images.