Anomaly Localization

Anomaly localization aims to pinpoint atypical regions within data, such as defects in manufactured goods or lesions in medical images. Current research heavily utilizes deep learning, focusing on autoencoders, transformers, and generative models to learn normal patterns and identify deviations, often employing techniques like reconstruction error analysis, score matching, and knowledge distillation. This field is crucial for various applications, including industrial quality control, medical diagnosis, and security systems, offering the potential for improved efficiency, accuracy, and safety.