Label Enhancement

Label enhancement techniques aim to improve the quality and quantity of training data labels, addressing challenges in various machine learning tasks. Current research focuses on developing methods that leverage existing labels to generate more informative or complete label distributions, often employing contrastive learning, graph-based approaches, or teacher-student models to achieve this. These advancements are significant because they enable more efficient use of limited labeled data, improving model performance and reducing the high cost associated with manual annotation, particularly in applications like multi-label learning and 3D object detection. The resulting enhanced labels lead to more robust and accurate models across diverse domains.