Weak Label

Weak label learning addresses the challenge of training machine learning models with imperfect or incomplete labels, a common scenario in many domains where obtaining high-quality annotations is expensive or difficult. Current research focuses on leveraging various strategies, including data programming with clinician-in-the-loop approaches, incorporating reliability estimates into model alignment, and using large language models to generate or refine weak labels, often in conjunction with techniques like self-training and transfer learning. This field is crucial for advancing machine learning applications in areas like medical image analysis, natural language processing, and other fields where fully labeled datasets are scarce, enabling the development of robust and accurate models with limited annotation resources.