Model interpretability is a key challenge that has yet to align with the advancements observed in contemporary state-of-the-art deep learning models. In particular, deep learning aided vision tasks require interpretability, in order for their adoption in more specialized domains such as medical imaging. Although the field of explainable AI (XAI) developed methods for interpreting vision models along with early convolutional neural networks, recent XAI research has mainly focused on assigning attributes via saliency maps. As such, these methods are restricted to providing explanations at a sample level, and many explainability methods suffer from low adaptability across a wide range of vision models. In our work, we re-think vision-model explainability from a novel perspective, to probe the general input structure that a model has learnt during its training. To this end, we ask the question: "How would a vision model fill-in a masked-image". Experiments on standard vision datasets and pre-trained models reveal consistent patterns, and could be intergrated as an additional model-agnostic explainability tool in modern machine-learning platforms. The code will be available at \url{https://github.com/BoTZ-TND/FillingTheBlanks.git}

翻译：模型可解释性是一个关键挑战，其发展尚未与当代先进深度学习模型所取得的进展相匹配。特别是在深度学习辅助的视觉任务中，可解释性对于其在医疗影像等专业领域的应用至关重要。尽管可解释人工智能（XAI）领域已针对早期卷积神经网络（CNN）开发了视觉模型解释方法，但近期的XAI研究主要集中于通过显著图进行属性归因。因此，这些方法仅限于提供样本层面的解释，且许多可解释性方法在广泛视觉模型中的适应性较低。在本研究中，我们从全新视角重新思考视觉模型的可解释性，旨在探究模型在训练过程中学习到的通用输入结构。为此，我们提出核心问题：“视觉模型将如何填补被遮蔽的图像？”在标准视觉数据集与预训练模型上的实验揭示了一致的规律，该方法可作为模型无关的可解释性工具集成至现代机器学习平台中。代码发布于 \url{https://github.com/BoTZ-TND/FillingTheBlanks.git}