Label Efficiency

Label efficiency in machine learning focuses on minimizing the need for labeled data during model training, a crucial aspect given the high cost and time associated with data annotation. Current research emphasizes techniques like active learning, which strategically selects data points for labeling, and leveraging pre-trained models (foundation models) to improve generalization from limited labeled data. These advancements are significant because they enable the application of deep learning to domains with scarce labeled data, such as medical imaging and remote sensing, while also reducing the overall cost and effort required for model development. Furthermore, research explores innovative approaches such as program generation for annotation and the use of ensemble methods to improve both efficiency and accuracy.