H$^2$EDL: Hyper Evidential Deep Learning for Hierarchical Classification
Bridging hierarchical classification and uncertainty for more robust, reliable machine learning.
Fine-grained recognition often involves hierarchical label spaces, where a model may be confident about a coarse semantic concept while remaining uncertain among its descendant classes. Such structured ambiguity requires uncertainty representations that capture both fine-grained ...
ArXiv CS.LG
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H2EDL effectively bridges the gap between hierarchical classification and uncertainty quantification by leveraging taxonomy structures to resolve structured ambiguity. This approach ensures that deep learning models maintain consistent, calibrated probability estimates across both coarse and fine-grained class boundaries.
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