Prior Based Loss

Prior-based loss functions are being actively researched to improve the performance and robustness of machine learning models, particularly in image analysis and related fields. Current work focuses on integrating prior knowledge, either low-level (e.g., derived from ground truth data) or high-level (e.g., expert-defined anatomical constraints), directly into the loss function to guide model training and enhance results. This approach is applied across various architectures, including convolutional neural networks and transformers, and shows promise in improving accuracy and efficiency for tasks such as image segmentation, classification, and retrieval, particularly where data is limited or noisy. The ultimate goal is to leverage prior information to create more reliable and accurate models with improved generalization capabilities.