Annotation Cost

Annotation cost, the expense of labeling data for training machine learning models, is a major bottleneck in many fields, driving research into cost-effective strategies. Current efforts focus on active learning techniques, leveraging pre-trained models (like vision-language models and transformers) to intelligently select the most informative data for annotation, and exploring methods to balance annotation quality and quantity. Reducing annotation costs is crucial for advancing AI applications, particularly in resource-constrained settings and domains requiring specialized expertise, ultimately improving the accessibility and scalability of machine learning.