Labeling Effort

Labeling effort, the time and resources required to annotate data for machine learning, is a significant bottleneck in many fields. Current research focuses on minimizing this effort through techniques like active learning (strategically selecting data points for labeling), semi-supervised learning (leveraging both labeled and unlabeled data), and synthetic data generation. These approaches, often employing models such as U-Nets and transformers, aim to achieve comparable model performance with drastically reduced labeling needs, impacting diverse applications from medical image analysis to software development. The ultimate goal is to improve the efficiency and scalability of machine learning by reducing reliance on extensive manual annotation.