BAAF: Universal Transformation of One-Class Classifiers for Unsupervised Image Anomaly Detection

Detecting anomalies in images and video is an essential task for multi-ple real-world problems, including industrial inspection, computer-assisted diag-nosis, and environmental monitoring. Anomaly detection is typically formulatedas a one-class classification problem, where the training data consists solely ofnominal values, leaving methods built on this assumption susceptible to train-ing label noise. We present Bootstrap Aggregation Anomaly Filtering (BAAF),a method that transforms an arbitrary one-class classifier-based anomaly detec-tor into a fully unsupervised method. This is achieved by leveraging the uniqueintrinsic properties of anomaly detection: anomalies are uncommon in the sam-pled data and generally heterogeneous. These properties enable us to design amodified Bootstrap Aggregation method that uses multiple independently trainedinstances of supervised one-class classifiers to filter the training dataset for anoma-lies. This transformation requires no modifications to the underlying anomaly de-tector; only the algorithmically selected data bags used for training change. Wedemonstrate empirically that our method can transform a wide variety of one-classclassifier-based image anomaly detectors into unsupervised ones. Consequently,we present the first fully unsupervised logical anomaly detection method for im-ages. We also demonstrate that our method achieves state-of-the-art performancein fully unsupervised anomaly detection on the MVTec AD, ViSA, Real-IAD andMvTec LOCO AD datasets. As improvements to one-class classifiers are made,our method directly transfers those improvements to the unsupervised domain,linking the domains.