Self Supervised Anomaly

Self-supervised anomaly detection (SSAD) aims to identify unusual data points without relying on labeled anomalous examples, leveraging only normal data for training. Current research focuses on developing robust feature representations using techniques like contrastive learning, autoencoders, and vision transformers, often incorporating data augmentation strategies to improve model generalization. This approach is particularly valuable in domains with limited labeled data, such as medical imaging and industrial quality control, offering the potential for more efficient and effective anomaly detection in diverse applications. The development of better label-free evaluation methods for SSAD models remains a key challenge.