Manual Label

Manual labeling, the process of assigning tags or annotations to data, is crucial for training machine learning models, particularly in image analysis, natural language processing, and other data-rich fields. Current research focuses on improving labeling efficiency through techniques like active learning, human-in-the-loop systems, and automated labeling methods using large language models and deep neural networks (e.g., U-Net, transformers). These advancements aim to reduce the cost and time associated with manual labeling, enabling the development of more accurate and robust models across diverse applications, from medical image analysis to infrastructure inspection.