Label Relationship

Label relationships, encompassing the dependencies and correlations between different labels in a dataset, are a crucial area of research aiming to improve the accuracy and robustness of machine learning models. Current efforts focus on leveraging label relationships through graph-based models, such as hypergraph neural networks, and incorporating these relationships into the learning process via novel loss functions and attention mechanisms. Understanding and effectively utilizing label relationships is vital for addressing challenges like spurious correlations, improving generalization performance, and enhancing the interpretability of models across diverse applications, including image classification, natural language processing, and drug interaction prediction.