Anomaly Detection Framework

Anomaly detection frameworks aim to identify unusual patterns or events within datasets, a crucial task across diverse fields. Current research emphasizes developing robust and efficient methods applicable across various data types (images, time series, text, 3D point clouds) and domains, focusing on architectures like autoencoders, transformers, diffusion models, and normalizing flows, often incorporating self-supervised learning and multi-modal approaches to improve performance and interpretability. These advancements are significantly impacting various sectors, including manufacturing quality control, cybersecurity threat detection, and medical image analysis, by enabling more accurate and timely identification of critical anomalies.