填空：重新思考视觉中的可解释性
Fill in the blanks: Rethinking Interpretability in vision
AI总结：
针对视觉模型可解释性在样本级受限和适应性低的问题，本文提出通过探究模型填补掩码图像的方式来分析其学习到的输入结构，实验证明该方法可作为模型无关的稳定可解释性工具。
摘要：
模型可解释性是一个关键挑战，尚未与当代最先进的深度学习模型中观察到的进展相一致。特别是，深度学习辅助的视觉任务需要可解释性，以便其在医学成像等更专业领域中被采用。尽管可解释AI（XAI）领域开发了用于解释视觉模型及早期卷积神经网络的方法，但最近的XAI研究主要集中在通过显著性图分配属性。因此，这些方法仅限于在样本级别提供解释，并且许多可解释性方法在广泛的视觉模型中适应性较低。在我们的工作中，我们从一个新颖的视角重新思考视觉模型的可解释性，以探究模型在训练期间学习到的通用输入结构。为此，我们提出问题：“视觉模型将如何填补被掩码的图像”。在标准视觉数据集和预训练模型上的实验揭示了稳定的模式，并且可以作为现代机器学习平台中额外的模型无关可解释性工具被集成。代码将发布于https://github.com/BoTZ-TND/FillingTheBlanks.git。
英文摘要：
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}
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
