Distribution Anomaly Detection

Distribution anomaly detection focuses on identifying data points that deviate significantly from the expected distribution of "normal" data, a crucial task in various applications like cybersecurity and medical imaging. Current research emphasizes developing robust methods that handle complex data distributions, including exploring multi-scale representations (e.g., combining global and local image features) and leveraging self-supervised learning techniques such as masked autoencoders. These advancements aim to improve the accuracy and reliability of anomaly detection, particularly in high-dimensional and large-scale datasets, leading to more effective systems for identifying malicious activities or medical abnormalities.