# A neurosymbolic Approach with Epistemic Deep Learning for Hierarchical Image Classification Source: [A neurosymbolic Approach with Epistemic Deep Learning for Hierarchical Image Classification]() Collection index: [llms.txt]() Data updated: 2026-05-20T05:00:05.687Z Authors: Ezel Kilicdere, Shireen Kudukkil Manchingal, Fabio Cuzzolin Published: 2026-05-11T09:43:43.000Z ## Abstract 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. We propose, for the first time, a unified neurosymbolic and epistemic modelling framework that augments Swin Transformers with focal set reasoning and differentiable fuzzy logic. Rather than treating labels as isolated categories, our method induces data-driven focal sets within the learnt embedding space, which helps capture epistemic uncertainty over multiple plausible fine-grained classes. These focal sets form the basis of a belief-theoretic layer that uses fuzzy membership functions and t-norm conjunctions to encourage consistency between fine- and coarse-grained predictions. A learnable loss further balances calibration, mass regularisation, and logical consistency, allowing the model to adaptively trade off symbolic structure with data-driven evidence. In experiments on hierarchical image classification, our framework maintains accuracy on par with transformer baselines while providing more calibrated and interpretable predictions, reducing overconfidence and enforcing high logical consistency across hierarchical outputs. Our experimental results show that combining focal set reasoning with fuzzy logic provides a practical step toward deep learning models that are both accurate and epistemically aware. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Hierarchical Image Classification on iNaturalist 2021](): Hierarchical Image Classification; iNaturalist 2021; Accuracy (fine): 85.12. - [Hierarchical Image Classification on CIFAR-100](): Hierarchical Image Classification; CIFAR-100; Accuracy (fine): 86.04. ## Original sources and implementation [Original paper]() [PDF]()