统一解释梯度归因方法的异常行为，揭示其本质是激活神经元权重对齐。
Unifying Perplexing Behaviors in Modified BP Attributions through Alignment Perspective
- 通过激活神经元权重对齐，统一解释多种归因方法的行为
- 预测新现象并降低对随机权重的敏感性，提升可解释性可靠性
- 适合研究模型可解释性与归因机制的学者阅读
归因方法旨在识别影响模型决策的关键输入像素。主流方法采用改进的反向传播（BP）规则来逆向推导决策过程，相比原始梯度提升了可解释性。然而，这些方法缺乏坚实的理论基础，表现出令人困惑的行为，如对参数随机化不敏感，引发对其可靠性的担忧，亟需理论支撑。本文提出一个统一的理论框架，涵盖GBP、RectGrad、LRP和DTD等方法，证明它们通过组合激活神经元的权重实现输入对齐，从而提升可视化质量并降低对权重随机化的敏感性。主要贡献包括：(1) 统一解释多种行为，而非仅关注单一现象；(2) 准确预测新的行为模式；(3) 揭示决策过程中的层间信息变化及归因与模型决策的关系。
原文摘要 · Abstract (English)
Attributions aim to identify input pixels that are relevant to the decision-making process. A popular approach involves using modified backpropagation (BP) rules to reverse decisions, which improves interpretability compared to the original gradients. However, these methods lack a solid theoretical foundation and exhibit perplexing behaviors, such as reduced sensitivity to parameter randomization, raising concerns about their reliability and highlighting the need for theoretical justification. In this work, we present a unified theoretical framework for methods like GBP, RectGrad, LRP, and DTD, demonstrating that they achieve input alignment by combining the weights of activated neurons. This alignment improves the visualization quality and reduces sensitivity to weight randomization. Our contributions include: (1) Providing a unified explanation for multiple behaviors, rather than focusing on just one. (2) Accurately predicting novel behaviors. (3) Offering insights into decision-making processes, including layer-wise information changes and the relationship between attributions and model decisions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。