Prior Correction

Prior correction in machine learning focuses on integrating pre-existing knowledge or assumptions (priors) into models to improve performance, particularly when data is limited or noisy. Current research explores diverse methods for incorporating priors, ranging from embedding prior knowledge directly into model architectures (e.g., using learned priors in generative models or attention mechanisms guided by prior information) to employing priors to regularize optimization processes during training or inference. This approach is proving valuable across numerous applications, including image processing, medical image analysis, and causal inference, by enhancing model accuracy, robustness, and interpretability, especially in challenging scenarios with limited data or complex data distributions.