Synthetic Anomaly

Synthetic anomaly detection focuses on improving anomaly detection models by generating artificial anomalies to augment training data, addressing the scarcity of real-world anomalous examples. Current research emphasizes developing domain-agnostic methods that create diverse and hard-to-distinguish synthetic anomalies, often employing techniques like contrastive learning, diffusion processes, and neural networks (including autoencoders and masked autoencoders). This approach enhances model performance, particularly in areas like image and time-series analysis, and offers theoretical guarantees for improved accuracy and generalization in various applications, including medical imaging and industrial anomaly detection.