Label Embeddings

Label embeddings represent categorical data, such as labels in classification tasks, as dense vectors in a continuous space, aiming to capture semantic relationships between labels and improve model performance. Current research focuses on integrating label embeddings with various architectures, including contrastive learning, transformers, and graph convolutional networks, to enhance tasks like multi-label classification, hierarchical text classification, and anomaly detection. This approach improves model accuracy, efficiency, and interpretability across diverse applications, from medical image analysis to natural language processing, by leveraging the rich semantic information encoded within label embeddings. The resulting advancements contribute to more robust and effective machine learning models across numerous domains.