Constraint Loss

Constraint loss in machine learning focuses on incorporating prior knowledge or desired properties into the training process, improving model accuracy and reliability by explicitly penalizing outputs that violate predefined constraints. Current research explores various constraint implementation methods, including hard constraints via optimization techniques like sequential quadratic programming and soft constraints integrated into loss functions, often applied to Physics-Informed Neural Networks (PINNs) and other architectures like transformers. This approach is proving valuable across diverse fields, enhancing the performance of models in tasks ranging from time series forecasting and object detection to solving partial differential equations and adapting to unseen data distributions in unsupervised domain adaptation.