Deviation Detection

Deviation detection focuses on identifying anomalies or unexpected variations from established norms or expected patterns across diverse domains. Current research emphasizes developing robust methods, including transformer-based approaches and adaptations of the Hough transform, to detect these deviations in visual data, process executions, and even within federated learning systems. This work is crucial for improving quality control in manufacturing, enhancing risk management in complex processes, and securing machine learning models against adversarial attacks, ultimately leading to more efficient and reliable systems. The development of context-aware frameworks is a growing trend, aiming to improve the accuracy and interpretability of deviation detection in dynamic environments.