Unlabeled Anomaly

Unlabeled anomaly detection focuses on identifying unusual data points or patterns without relying on labeled examples of anomalies, a significant challenge across diverse fields. Current research emphasizes developing robust models that can effectively capture complex data distributions, often employing deep neural networks (like variational autoencoders and graph neural networks), Bayesian methods, and collaborative filtering techniques to achieve this. These advancements are crucial for applications ranging from cybersecurity and industrial quality control to healthcare monitoring and satellite telemetry, where labeled anomaly data is scarce or expensive to obtain. The development of interpretable methods that explain anomaly detection decisions is also a growing area of focus.