Reconstruction Based Anomaly Detection

Reconstruction-based anomaly detection aims to identify deviations from normal patterns by training models to reconstruct typical data and flagging instances with high reconstruction errors as anomalies. Current research focuses on improving the robustness and accuracy of these methods, particularly for multi-class problems and high-dimensional data, employing architectures like autoencoders, transformers, variational autoencoders, and diffusion models. This approach is significant for its applicability across diverse domains, including medical imaging, fraud detection, and industrial process monitoring, offering a powerful unsupervised technique for identifying unusual events where labeled anomaly data is scarce or unavailable.