Anomaly Score

Anomaly scoring is a crucial aspect of anomaly detection, aiming to quantify the deviation of data points from a learned normal distribution. Current research emphasizes developing robust and interpretable anomaly scores, often leveraging generative models (like variational autoencoders and diffusion models), metric learning, and various clustering techniques to achieve this. These advancements are driving improvements in diverse applications, including industrial quality control, medical image analysis, and financial fraud detection, by enabling more accurate and reliable identification of unusual patterns. Furthermore, research is actively addressing challenges such as handling imbalanced datasets, contaminated data, and the need for explainability in anomaly detection systems.