Human Annotated Label

Human-annotated labels are crucial for training machine learning models, but their creation is expensive and time-consuming. Current research focuses on mitigating this limitation through techniques like crowdsourcing with noise reduction, generating synthetic labels (e.g., using geometric approaches or large language models), and employing active learning strategies to prioritize the most informative labels. These efforts aim to improve model performance while reducing reliance on extensive human annotation, impacting various fields from medical image analysis to natural language processing and impacting the efficiency and scalability of machine learning applications.