Weakly Annotated

Weakly annotated data leverages readily available, less precise labels (e.g., image-level classifications instead of pixel-level) to train machine learning models, addressing the high cost and time associated with meticulous data annotation. Current research focuses on techniques like contrastive learning, self-consistency learning, and transfer learning with large language models to effectively utilize this less expensive data, often achieving performance comparable to fully supervised methods. This approach is particularly impactful in fields like medical image analysis and natural language processing where acquiring high-quality annotations is challenging, enabling the development of robust models with limited labeled data. The resulting efficiency gains significantly broaden the scope of machine learning applications.