Anomaly Detection

Anomaly detection focuses on identifying unusual patterns or deviations from expected behavior within data, aiming to improve system reliability and safety across diverse applications. Current research emphasizes unsupervised and self-supervised learning approaches, employing architectures like autoencoders, transformers, and graph neural networks, often incorporating techniques such as Bayesian inference and metric learning to enhance robustness and interpretability. The field's significance stems from its broad applicability, ranging from fraud detection and medical diagnosis to industrial process monitoring and network security, with ongoing efforts to develop more efficient, accurate, and explainable methods.