Semantic Anomaly Detection

Semantic anomaly detection focuses on identifying deviations from expected behavior not solely based on statistical rarity, but also on the meaning or context of the observed data. Current research emphasizes the use of large language models and diffusion models, along with techniques like sequence-to-sequence learning and multi-instance learning, to improve the accuracy and explainability of anomaly detection across diverse applications such as process mining, video surveillance, and autonomous systems. This field is significant because it enables more robust and insightful anomaly detection, moving beyond simple statistical approaches to understand the underlying causes of unusual events, leading to improved decision-making and system reliability.