Anomalous Image

Anomalous image detection focuses on identifying deviations from expected patterns in images, crucial for various applications like medical diagnosis and industrial quality control. Current research heavily utilizes generative models, particularly diffusion models and autoencoders, often incorporating techniques like attention mechanisms and memory modules to improve anomaly localization and classification, even in scenarios with limited anomalous training data. These advancements are significantly impacting fields requiring robust anomaly detection, enabling more efficient and accurate analysis across diverse image types and applications, from medical imaging to industrial inspection.