Multi Annotator

Multi-annotator learning addresses the challenges of training machine learning models on datasets labeled by multiple humans, whose annotations often contain noise, inconsistencies, and varying levels of expertise. Current research focuses on developing methods to effectively aggregate these diverse annotations, leveraging techniques like multi-rater learning, probabilistic frameworks, and active learning strategies to improve model accuracy and robustness. This field is crucial for improving the reliability of machine learning models across various domains, from medical image analysis and natural language processing to visual question answering, where human subjectivity and ambiguity are inherent. The development of robust multi-annotator methods directly impacts the quality and generalizability of AI systems.