# Transferring Visual Explainability of Self-Explaining Models to Prediction-Only Models without Additional Training Source: [Transferring Visual Explainability of Self-Explaining Models to Prediction-Only Models without Additional Training]() Collection index: [llms.txt]() Data updated: 2026-02-04T05:00:24.095Z Authors: Yuya Yoshikawa, Ryotaro Shimizu, Takahiro Kawashima, Yuki Saito Published: 2025-07-06T12:55:31.000Z ## Abstract 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. ## 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. ## Original sources and implementation [Original paper]() [PDF]()