Multi Class Unsupervised Anomaly Detection

Multi-class unsupervised anomaly detection aims to identify anomalies across multiple classes using only normal training data, a challenging problem addressed by recent research. Current efforts focus on developing unified models that avoid the limitations of class-specific approaches, employing architectures like transformers, state-space models, and diffusion models to improve anomaly detection accuracy. These advancements are significant because they enable more efficient and generalizable anomaly detection systems, with applications ranging from industrial quality control to medical image analysis. The field is actively exploring methods to mitigate issues like "learning shortcuts" and improve the robustness and interpretability of these models.