Absolute-Unified Multi-Class Anomaly Detection via Class-Agnostic Distribution Alignment

arXiv:2404.00724v1 Announce Type: new
Abstract: Conventional unsupervised anomaly detection (UAD) methods build separate models for each object category. Recent studies have proposed to train a unified model for multiple classes, namely model-unified UAD. However, such methods still implement the unified model separately on each class during inference with respective anomaly decision thresholds, which hinders their application when the image categories are entirely unavailable. In this work, we present a simple yet powerful method to address multi-class anomaly detection without any …

abstract alignment anomaly anomaly detection arxiv build class cs.cv decision detection distribution however inference multiple object studies train type unified model unsupervised via