In the field of multi-class anomaly detection, reconstruction-based methods derived from
single-class anomaly detection face the well-known challenge of “learning shortcuts” …

Anomaly detection (AD) is a machine learning task that identifies anomalies by learning
patterns from normal training data. In many real-world scenarios, anomalies vary in severity …

Recent studies highlighted a practical setting of unsupervised anomaly detection (UAD) that
builds a unified model for multi-class images, serving as an alternative to the conventional …

This study explores the recently proposed challenging multi-view Anomaly Detection (AD)
task. Single-view tasks would encounter blind spots from other perspectives, resulting in …

This work studies a challenging and practical issue known as multi-class unsupervised
anomaly detection (MUAD). This problem requires only normal images for training while …

Recent advancements in anomaly detection have shifted focus towards Multi-class Unified
Anomaly Detection (MUAD), offering more scalable and practical alternatives compared to …