Unsupervised Anomaly Detection

Unsupervised anomaly detection aims to identify unusual data points without relying on labeled examples, focusing on learning the characteristics of normal data to distinguish deviations. Current research emphasizes developing robust models using architectures like autoencoders, diffusion probabilistic models, and graph neural networks, often incorporating techniques such as test-time adaptation, knowledge distillation, and generative adversarial networks to improve accuracy and efficiency. This field is crucial for various applications, including medical image analysis, industrial quality control, and cybersecurity, where detecting rare events is critical but labeled data is scarce or expensive to obtain. The development of more efficient and interpretable methods remains a key focus.