Label Space

Label space, encompassing the set of all possible labels or categories in a machine learning task, is a critical area of research focusing on improving the efficiency and accuracy of models, particularly in scenarios with numerous or inconsistent labels. Current research emphasizes methods for unifying disparate label spaces across multiple datasets, often employing graph neural networks or language embeddings to resolve semantic inconsistencies and improve model generalization. This work is crucial for scaling machine learning to real-world applications involving large, complex datasets, such as image segmentation, document layout analysis, and extreme multi-label classification, ultimately leading to more robust and reliable models.