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Label Hierarchy Transition: Delving Into Class Hierarchies to Enhance Deep Classifiers
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Summary
Label Hierarchy Transition (LHT) is a deep learning framework for hierarchical classification, which sorts objects into multi-level categories such as order, family, and species. Existing methods split the task into separate multi-class problems, which fails to capture correlations across hierarchy levels. LHT pairs a transition network, which learns label hierarchy transition matrices, with a confusion loss that encourages the classifier to learn cross-level correlations. The authors report that it outperforms current state-of-the-art methods on public benchmarks and extend it to skin lesion diagnosis.