Additional experiments

PermGrad additional experiments and revision updates

This release provides the updated experimental and reproducibility materials associated with the revised version of the manuscript:

PermGrad: Interpretable Hybrid Neural Networks with Synthetic Images for Tabular Data

PermGrad is an interpretability framework for Hybrid Neural Networks (HyNNs) that jointly process original tabular features and TINTO-generated synthetic images. The architecture combines an MLP branch, which captures symbolic/global dependencies in the original tabular space, with a CNN branch, which learns spatially organized feature interactions from synthetic image representations. The proposed PermGrad mechanism integrates permutation-based feature importance, Grad-CAM saliency, and pruning-based branch contribution weights into a unified, modality-aware global feature attribution score.

Main updates in this release

• Added additional resolution ablation experiments for TINTO-generated synthetic images using 10×10, 20×20, and 30×30 image sizes.
• Included quantitative performance comparisons across image resolutions for representative classification and regression datasets.
• Extended the class-imbalance robustness analysis to both GAS and Covertype datasets.
• Added Spearman correlation analyses between class size and Grad-CAM saliency/entropy to assess whether PermGrad exhibits majority-class saliency bias.
• Added feature-ranking stability analysis under class-balanced retraining.
• Updated interpretability analysis to distinguish between moderate imbalance and extreme imbalance scenarios.
• Improved the visual structure and readability of the HyNN–PermGrad workflow figure.
• Revised terminology related to synthetic image interpretability, replacing claims of exact 1-to-1 feature–pixel mapping with more precise notions of deterministic feature localization and pixel-level traceability.
• Updated manuscript-related materials, figures, and tables to reflect the latest revision.

Repository contents

This repository includes the code, notebooks, figures, and reproducibility resources required to reproduce the experiments reported in the manuscript, including:

• TINTO-based tabular-to-image generation.
• MLP, CNN, and HyNN model training.
• Permutation-based feature importance for the MLP branch.
• Grad-CAM saliency extraction for the CNN branch.
• PermGrad branch-weighted feature attribution.
• Resolution ablation experiments.
• Class-imbalance robustness analysis.
• Additional interpretability and visualization scripts.

Notes

This release is intended to preserve the experimental state associated with the revised manuscript submission. The corresponding Zenodo archive provides a citable snapshot of the repository.