Anomaly Generation

Anomaly generation focuses on creating synthetic anomalous data to address the scarcity of real-world anomaly samples, a critical limitation in anomaly detection. Current research emphasizes developing generative models, particularly diffusion-based approaches, that produce realistic and diverse anomalies, often incorporating techniques like spatial embedding and attention mechanisms to improve anomaly-mask alignment. This work is significant because it enhances the performance of anomaly detection systems across various applications, from industrial quality control to medical image analysis, by providing more robust training data for these systems. The field is also actively exploring methods for model selection and hyperparameter optimization in the absence of labeled data, leveraging techniques like synthetic anomaly generation for validation.