Label hierarchies are often available apriori as part of biological taxonomy
or language datasets WordNet. Several works exploit these to learn hierarchy
aware features in order to improve the classifier to make semantically
meaningful mistakes while maintaining or reducing the overall error. In this
paper, we propose a novel approach for learning Hierarchy Aware Features (HAF)
that leverages classifiers at each level of the hierarchy that are constrained
to generate predictions consistent with the label hierarchy. The classifiers
are trained by minimizing a Jensen-Shannon Divergence with target soft labels
obtained from the fine-grained classifiers. Additionally, we employ a simple
geometric loss that constrains the feature space geometry to capture the
semantic structure of the label space. HAF is a training time approach that
improves the mistakes while maintaining top-1 error, thereby, addressing the
problem of cross-entropy loss that treats all mistakes as equal. We evaluate
HAF on three hierarchical datasets and achieve state-of-the-art results on the
iNaturalist-19 and CIFAR-100 datasets. The source code is available at
this https URL

提出了一种学习层次感知特征的方法，该方法利用层次结构中的分类器并使用 Jensen-Shannon Divergence 和几何损失来训练，可以在维持 top-1 误差的情况下改善分类器错误，从而提高分类性能。