Top $K$ Classification

Top-$k$ classification extends standard single-label classification by predicting the $k$ most likely classes for an input, addressing scenarios with inherent ambiguity or the need for multiple relevant labels. Current research focuses on developing novel loss functions and algorithms that optimize both prediction accuracy and the number of predicted classes (cardinality), often employing techniques like cost-sensitive learning and surrogate losses with provable consistency guarantees. These advancements improve the performance of various models, from ensemble methods combining large language models to deep learning architectures for image classification, impacting fields like information retrieval and image recognition by providing more robust and informative predictions.