A neurosymbolic Approach with Epistemic Deep Learning for Hierarchical Image Classification

ArXi:2605.16383v1 Announce Type: new Deep neural networks achieve high accuracy on image classification tasks. Yet, they often produce overconfident predictions as which fail to express epistemic uncertainty, and frequently violate logical or structural constraints present in the data. These limitations are particularly pronounced in hierarchical classification, where predictions across fine and coarse levels must remain coherent.