Weakly Labeled Data

Weakly labeled data leverages readily available, but imperfectly annotated, datasets to train machine learning models, addressing the high cost and time associated with creating fully labeled datasets. Current research focuses on integrating weakly labeled data with techniques like semi-supervised learning, leveraging large language models (LLMs) for label generation, and developing novel loss functions that account for the uncertainty inherent in weak labels. This approach is particularly impactful in domains like medical image analysis and natural language processing where acquiring high-quality labels is challenging, enabling the development of accurate and efficient models with limited resources.