Full Label

"Full label" research broadly addresses the challenges of efficiently and effectively utilizing labeled data in machine learning, encompassing both the quality and quantity of labels. Current research focuses on reducing labeling effort through techniques like single-point prompts, active learning strategies that prioritize informative samples, and self-supervised learning methods that leverage unlabeled data. These advancements aim to improve model robustness to noisy labels, enhance model interpretability, and enable effective training with limited annotations, ultimately impacting various applications from medical image analysis to natural language processing.