Label Representation

Label representation research focuses on improving how machine learning models understand and utilize labels in classification and other tasks. Current efforts concentrate on developing more sophisticated label representations beyond simple one-hot encodings, often incorporating semantic information, hierarchical structures, and external knowledge sources, utilizing techniques like contrastive learning, graph convolutional networks, and retrieval-augmented methods within various model architectures (e.g., deep convolutional networks, transformers). These advancements lead to improved model performance, particularly in few-shot learning and weakly supervised settings, impacting diverse applications from medical diagnosis to natural language processing.