evidential-deep-learning

The paper proposes H2EDL, a hyper evidential deep learning model for hierarchical classification that captures uncertainty at multiple levels of a label hierarchy, reducing calibration error and improving the preservation of coarse categories in fine-grained recognition tasks.

A mathematically principled framework, Variational Inference Evidential Deep Learning (VI-EDL), is proposed to address limitations in conventional Evidential Deep Learning by reformulating it through variational inference, deriving an Evidence Lower Bound, establishing a generalization bound, and achieving state-of-the-art performance on visual and medical datasets.