Label Hierarchy Transition: Delving Into Class Hierarchies to Enhance Deep Classifiers

This paper presents Label Hierarchy Transition (LHT), a deep learning framework designed to improve hierarchical classification, which is the task of sorting objects into multi-level category structures (like organizing a bird into order, family, and species levels). Unlike existing methods that break hierarchical classification into separate classification tasks, LHT uses a transition network and a confusion loss to better capture the relationships between categories at different levels of the hierarchy. The researchers tested their approach on benchmark datasets and a skin lesion diagnosis task, showing improvements over existing methods.