A neurosymbolic Approach with Epistemic Deep Learning for Hierarchical Image Classification

• URL: http://arxiv.org/abs/2605.16383v1
• Date: Mon, 11 May 2026 09:43:43 GMT
• Title: A neurosymbolic Approach with Epistemic Deep Learning for Hierarchical Image Classification
• Abstract summary: We propose a unified neurosymbolic and epistemic modelling framework to augment Swin Transformers.<n>Rather than treating labels as isolated categories, our method induces data-driven focal sets.<n>These focal sets form the basis of a belief-theoretic layer.<n>A learnable loss balances calibration, mass regularisation, and logical consistency, allowing the model to adaptively trade off symbolic structure with data-driven evidence.
• Score: 7.18839452757154
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

Related papers

• SJEPA: Learning Elegant Latent Dynamics with Hybrid Symbolic-Neural Predictors [0.0]
  We introduce SJEPA, a reconstruction-free JEPA framework that learns predictive representations whose induced dynamics admit compact symbolic descriptions.<n>We formalise this principle through induced-dynamics complexity, analyse predictive-coordinate non-identifiability, and show that unconstrained operator compression creates a direct shortcut to representation collapse.<n>The results expose a controllable trade-off among predictive fidelity, representation quality, symbolic parsimony, and symbolic-neural allocation.
  arXiv  Detail & Related papers  (2026-08-04T13:08:07Z)
• From Arithmetic to Logic: The Resilience of Logic and Lookup-Based Neural Networks Under Parameter Bit-Flips [0.6868507883617846]
  In this work, we study resilience as a structural property of neural architectures.<n>We show that lower precision, higher sparsity, bounded activations, and shallow depth are consistently favored.<n>We identify a novel even-layer recovery effect unique to logic-based architectures.
  arXiv  Detail & Related papers  (2026-03-24T03:53:04Z)
• Anatomy of a Lie: A Multi-Stage Diagnostic Framework for Tracing Hallucinations in Vision-Language Models [62.932580559941414]
  Vision-Language Models (VLMs) frequently "hallucinate" - generate plausible yet factually incorrect statements.<n>We propose a new paradigm for diagnosing hallucinations, recasting them from static output errors into dynamic pathologies of a model's computational cognition.
  arXiv  Detail & Related papers  (2026-03-16T17:20:38Z)
• An Orthogonal Learner for Individualized Outcomes in Markov Decision Processes [55.93922317950527]
  We develop a novel meta-learner called DRQ-learner.<n>Our DRQ-learner is applicable to settings with both discrete and continuous state spaces.
  arXiv  Detail & Related papers  (2025-09-30T15:49:29Z)
• Certified Neural Approximations of Nonlinear Dynamics [51.01318247729693]
  In safety-critical contexts, the use of neural approximations requires formal bounds on their closeness to the underlying system.<n>We propose a novel, adaptive, and parallelizable verification method based on certified first-order models.
  arXiv  Detail & Related papers  (2025-05-21T13:22:20Z)
• Pre-trained Models Succeed in Medical Imaging with Representation Similarity Degradation [6.545152478351316]
  The study establishes a rigorous problem definition centered on quantifying and analyzing representation similarity trajectories.<n>Our empirical findings reveal the potential existence of high-performance models that preserve both task accuracy and representation similarity to their pre-trained origins.
  arXiv  Detail & Related papers  (2025-03-11T01:37:54Z)
• LOGICSEG: Parsing Visual Semantics with Neural Logic Learning and Reasoning [73.98142349171552]
  LOGICSEG is a holistic visual semantic that integrates neural inductive learning and logic reasoning with both rich data and symbolic knowledge. During fuzzy logic-based continuous relaxation, logical formulae are grounded onto data and neural computational graphs, hence enabling logic-induced network training. These designs together make LOGICSEG a general and compact neural-logic machine that is readily integrated into existing segmentation models.
  arXiv  Detail & Related papers  (2023-09-24T05:43:19Z)
• Variational Classification [51.2541371924591]
  We derive a variational objective to train the model, analogous to the evidence lower bound (ELBO) used to train variational auto-encoders. Treating inputs to the softmax layer as samples of a latent variable, our abstracted perspective reveals a potential inconsistency. We induce a chosen latent distribution, instead of the implicit assumption found in a standard softmax layer.
  arXiv  Detail & Related papers  (2023-05-17T17:47:19Z)
• Neuro-symbolic model for cantilever beams damage detection [0.0]
  We propose a neuro-symbolic model for the detection of damages in cantilever beams based on a novel cognitive architecture. The hybrid discriminative model is introduced under the name Logic Convolutional Neural Regressor.
  arXiv  Detail & Related papers  (2023-05-04T13:12:39Z)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• A heteroencoder architecture for prediction of failure locations in porous metals using variational inference [1.2722697496405462]
  We employ an encoder-decoder convolutional neural network to predict the failure locations of porous metal tension specimens. The objective of predicting failure locations presents an extreme case of class imbalance since most of the material in the specimens do not fail. We demonstrate that the resulting predicted variances are effective in ranking the locations that are most likely to fail in any given specimen.
  arXiv  Detail & Related papers  (2022-01-31T20:26:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.