复现性评审："为什么不是其他类别"：迈向类对比反向传播解释
Reproducibility review of "Why Not Other Classes": Towards Class-Contrastive Back-Propagation Explanations
AI总结：
复现Wang & Wang 2022的类对比反向传播解释方法，验证其泛化性并指出原论文细节不足与方程错误，提供开源代码。
摘要：
"为什么不是其他类别？"：迈向类对比反向传播解释（Wang & Wang, 2022）提供了一种方法，用于对比性地解释为什么神经网络图像分类器中选择某一类别而非其他类别。该方法包括使用基于反向传播的解释方法，这些方法在softmax层之后而非之前应用。我们的工作包括复现原始论文中的工作。我们还通过评估该方法在XGradCAM、FullGrad和Vision Transformers上的表现来扩展论文，以评估其泛化能力。复现结果与原始论文相似，唯一区别是热图的可视化无法复现为相似外观。泛化能力总体良好，实现适用于Vision Transformers和替代反向传播方法。我们还表明，原始论文存在一些问题，如方法细节不足和错误方程，这使得复现变得困难。为解决这一问题，我们提供了一个开源仓库，包含本项目使用的所有代码。
英文摘要：
"Why Not Other Classes?": Towards Class-Contrastive Back-Propagation Explanations (Wang & Wang, 2022) provides a method for contrastively explaining why a certain class in a neural network image classifier is chosen above others. This method consists of using back-propagation-based explanation methods from after the softmax layer rather than before. Our work consists of reproducing the work in the original paper. We also provide extensions to the paper by evaluating the method on XGradCAM, FullGrad, and Vision Transformers to evaluate its generalization capabilities. The reproductions show similar results as the original paper, with the only difference being the visualization of heatmaps which could not be reproduced to look similar. The generalization seems to be generally good, with implementations working for Vision Transformers and alternative back-propagation methods. We also show that the original paper suffers from issues such as a lack of detail in the method and an erroneous equation which makes reproducibility difficult. To remedy this we provide an open-source repository containing all code used for this project.
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。
