General Anomaly Enhancement Network

General anomaly enhancement networks aim to identify deviations from expected patterns in diverse data types, such as sensor readings, images, and network structures. Current research focuses on developing unsupervised and weakly supervised learning methods, employing architectures like transformers and convolutional neural networks, often incorporating multi-resolution feature analysis or generative models to improve anomaly detection and localization accuracy. These advancements are significant for enhancing the robustness of autonomous systems, improving industrial quality control, and enabling more effective analysis of complex datasets across various scientific domains.