Unsupervised Detection

Unsupervised anomaly detection focuses on identifying unusual data points or patterns without relying on labeled examples, a crucial task in various fields where labeled data is scarce or expensive to obtain. Current research heavily utilizes generative models like variational autoencoders (VAEs), diffusion models, and normalizing flows, along with techniques like clustering and self-supervised learning, to learn representations of "normal" data and flag deviations. This approach has significant implications for diverse applications, including medical image analysis (detecting tumors, fetal brain anomalies), industrial quality control, and the detection of deepfakes and other forms of data manipulation. The development of robust and efficient unsupervised methods is driving progress in various scientific domains and improving the reliability of automated systems.