Label Regularization

Label regularization is a technique used to improve the performance and robustness of machine learning models, particularly deep learning models, by adding constraints to the learning process. Current research focuses on developing novel regularization methods tailored to specific challenges, such as handling noisy labels, improving transferability across different models, and optimizing hyperparameters in a data-efficient manner. These advancements leverage various techniques including bi-level optimization, consistency regularization, and instance-specific adaptations, impacting diverse applications from image processing and natural language processing to reinforcement learning. The ultimate goal is to enhance model generalization, reduce overfitting, and improve the reliability of predictions in various domains.