Normality Addition

Normality addition addresses the challenge of adapting machine learning models to evolving definitions of "normal" behavior, particularly crucial in anomaly detection. Current research focuses on methods that incorporate new normal patterns into pre-trained models, often leveraging techniques like vision-language models or regionally additive models to improve both accuracy and interpretability. These advancements are significant for various applications, including industrial process monitoring and image analysis, where the concept of normality can dynamically shift over time. The development of robust normality addition methods enhances the reliability and adaptability of anomaly detection systems in real-world scenarios.