# Transferring Visual Explainability of Self-Explaining Models to Prediction-Only Models without Additional Training

arXiv: https://arxiv.org/abs/2507.04380 · HTML: https://mlsanity.com/paper/2507.04380/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 49 references that are in the map.
- First posted on arXiv 6 Jul 2025.

## Builds on (most cited first, 30 of 32)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Neural Tangent Kernel: Convergence and Generalization in Neural Networks](https://mlsanity.com/paper/1806.07572/) (2018)
- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time](https://mlsanity.com/paper/2203.05482/) (2022)
- [Averaging Weights Leads to Wider Optima and Better Generalization](https://mlsanity.com/paper/1803.05407/) (2018)
- [Editing Models with Task Arithmetic](https://mlsanity.com/paper/2212.04089/) (2022)
- [Remote Sensing Image Scene Classification: Benchmark and State of the Art](https://mlsanity.com/paper/1703.00121/) (2017)
- [Robust fine-tuning of zero-shot models](https://mlsanity.com/paper/2109.01903/) (2021)
- [DataComp: In search of the next generation of multimodal datasets](https://mlsanity.com/paper/2304.14108/) (2023)
- [Towards Robust Interpretability with Self-Explaining Neural Networks](https://mlsanity.com/paper/1806.07538/) (2018)
- [Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations](https://mlsanity.com/paper/1703.03717/) (2017)
- [Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions](https://mlsanity.com/paper/2112.11561/) (2021)
- [Learning to Estimate Shapley Values with Vision Transformers](https://mlsanity.com/paper/2206.05282/) (2022)
- [Shapley Explanation Networks](https://mlsanity.com/paper/2104.02297/) (2021)
- [Bring Reason to Vision: Understanding Perception and Reasoning through Model Merging](https://mlsanity.com/paper/2505.05464/) (2025)
- [Guidelines and Evaluation of Clinical Explainable AI in Medical Image Analysis](https://mlsanity.com/paper/2202.10553/) (2022)
- [A Comprehensive Survey on Self-Interpretable Neural Networks](https://mlsanity.com/paper/2501.15638/) (2025)
- [On the Surprising Effectiveness of Attention Transfer for Vision Transformers](https://mlsanity.com/paper/2411.09702/) (2024)
- [Forgetting before Learning: Utilizing Parametric Arithmetic for Knowledge Updating in Large Language Models](https://mlsanity.com/paper/2311.08011/) (2023)
- [Efficient XAI Techniques: A Taxonomic Survey](https://mlsanity.com/paper/2302.03225/) (2023)
- [Bias Vector: Mitigating Biases in Language Models with Task Arithmetic Approach](https://mlsanity.com/paper/2412.11679/) (2024)
- [Strategies to exploit XAI to improve classification systems](https://mlsanity.com/paper/2306.05801/) (2023)
- [Saliency-Bench: A Comprehensive Benchmark for Evaluating Visual Explanations](https://mlsanity.com/paper/2310.08537/) (2023)
- [Efficient Shapley Values Estimation by Amortization for Text Classification](https://mlsanity.com/paper/2305.19998/) (2023)
- [Prediction via Shapley Value Regression](https://mlsanity.com/paper/2505.04775/) (2025)
- [On Fairness of Task Arithmetic: The Role of Task Vectors](https://mlsanity.com/paper/2505.24262/) (2025)

## Abstract (arXiv)

In image classification scenarios where both prediction and explanation efficiency are required, self-explaining models that perform both tasks in a single inference are effective. However, for users who already have prediction-only models, training a new self-explaining model from scratch imposes significant costs in terms of both labeling and computation. This study proposes a method to transfer the visual explanation capability of self-explaining models learned in a source domain to prediction-only models in a target domain based on a task arithmetic framework. Our self-explaining model comprises an architecture that extends Vision Transformer-based prediction-only models, enabling the proposed method to endow explanation capability to many trained prediction-only models without additional training. Experiments on various image classification datasets demonstrate that, except for transfers between less-related domains, the transfer of visual explanation capability from source to target domains is successful, and explanation quality in the target domain improves without substantially sacrificing classification accuracy.