Annotated Training Data

Annotated training data is crucial for training effective machine learning models, but acquiring and labeling such data is often expensive and time-consuming. Current research focuses on mitigating this limitation through techniques like synthetic data generation, semi-supervised learning (leveraging both labeled and unlabeled data), zero-shot learning (using prompts and large language models to bypass annotation), and active learning (strategically selecting data points for annotation). These advancements are significantly impacting various fields, enabling the development of accurate models in resource-constrained scenarios and accelerating progress in areas like medical image analysis, natural language processing, and object detection where large labeled datasets are traditionally difficult to obtain.