Anomaly Synthesis

Anomaly synthesis focuses on generating artificial anomalous data to improve anomaly detection models, particularly in scenarios with limited real-world anomaly samples. Current research emphasizes creating diverse and realistic synthetic anomalies using techniques like gradient ascent, diffusion models, and contrastive learning, often integrated with architectures such as normalizing flows and U-Nets. This approach is crucial for enhancing the performance of unsupervised anomaly detection methods across various domains, including industrial quality control and medical image analysis, leading to more robust and accurate systems for identifying deviations from normal patterns.