Anomaly Free

Anomaly-free data analysis focuses on developing methods to detect deviations from expected patterns in various data types, including images, sensor logs, and time series. Current research emphasizes unsupervised and self-supervised learning approaches, employing generative models, contrastive learning, and dimensionality reduction techniques like autoencoders and statistical methods to identify anomalies even with limited labeled data. These advancements are crucial for improving the reliability and efficiency of applications ranging from autonomous driving and industrial process monitoring to network security, where rapid and accurate anomaly detection is paramount.