标题： [回复] 提高视觉变压器的解释忠实度 显示英文标题

标题： [Re] Improving Interpretation Faithfulness for Vision Transformers

摘要： 这项工作旨在复现由arXiv:2311.17983提出的忠实视觉变换器(FViTs)的结果，以及来自arXiv:2012.09838和Xu (2022)等人的视觉变换器的可解释性方法。 我们研究了arXiv:2311.17983提出的主张，即使用扩散去噪平滑(DDS)可以提高可解释性在(1)分割任务中的鲁棒性以应对攻击，以及(2)分类任务中的扰动和攻击。 我们还通过研究作者的主张扩展了原始研究，即在任何可解释性方法中添加DDS可以提高其在攻击下的鲁棒性。 这在基线方法和最近提出的归因展开方法上进行了测试。 此外，我们测量了通过DDS获得FViT的计算成本和环境影响。 我们的结果总体上与原始研究的发现一致，尽管发现了某些细微差异并进行了讨论。 显示英文摘要

摘要： This work aims to reproduce the results of Faithful Vision Transformers (FViTs) proposed by arXiv:2311.17983 alongside interpretability methods for Vision Transformers from arXiv:2012.09838 and Xu (2022) et al. We investigate claims made by arXiv:2311.17983, namely that the usage of Diffusion Denoised Smoothing (DDS) improves interpretability robustness to (1) attacks in a segmentation task and (2) perturbation and attacks in a classification task. We also extend the original study by investigating the authors' claims that adding DDS to any interpretability method can improve its robustness under attack. This is tested on baseline methods and the recently proposed Attribution Rollout method. In addition, we measure the computational costs and environmental impact of obtaining an FViT through DDS. Our results broadly agree with the original study's findings, although minor discrepancies were found and discussed.