高斯平滑提升模型解释稳定性，但会降低准确性。
Gaussian Smoothing in Saliency Maps: The Stability-Fidelity Trade-Off in Neural Network Interpretability
- 引入高斯平滑改进梯度类解释方法的稳定性。
- 理论证明平滑能降低对训练随机性的敏感度。
- 适合关注解释结果稳定性的研究者使用。
梯度类显著性图被广泛用于解释神经网络分类器的决策并发现其学习到的规律。然而，标准梯度法生成的显著性图常因训练数据随机性和训练过程的随机性而高度敏感。本文研究了广义平滑算法（Smooth-Grad）中高斯平滑在提升梯度类显著性图对训练样本随机性鲁棒性中的作用。我们扩展了算法稳定性框架，证明了标准Simple-Grad、Integrated-Gradients和Smooth-Grad显著性图的稳定性误差上界。理论表明，高斯平滑有助于增强梯度类方法对训练设置随机性的稳定性。另一方面，我们分析了Smooth-Grad对原始Simple-Grad的忠实性，发现更强的高斯平滑会导致更低的忠实性。我们在标准图像数据集上进行了多项数值实验，结果验证了高斯平滑应用中的稳定性-忠实性权衡假设。
原文摘要 · Abstract (English)
Saliency maps have been widely used to interpret the decisions of neural network classifiers and discover phenomena from their learned functions. However, standard gradient-based maps are frequently observed to be highly sensitive to the randomness of training data and the stochasticity in the training process. In this work, we study the role of Gaussian smoothing in the well-known Smooth-Grad algorithm in the stability of the gradient-based maps to the randomness of training samples. We extend the algorithmic stability framework to gradient-based interpretation maps and prove bounds on the stability error of standard Simple-Grad, Integrated-Gradients, and Smooth-Grad saliency maps. Our theoretical results suggest the role of Gaussian smoothing in boosting the stability of gradient-based maps to the randomness of training settings. On the other hand, we analyze the faithfulness of the Smooth-Grad maps to the original Simple-Grad and show the lower fidelity under a more intense Gaussian smoothing. We support our theoretical results by performing several numerical experiments on standard image datasets. Our empirical results confirm our hypothesis on the fidelity-stability trade-off in the application of Gaussian smoothing to gradient-based interpretation maps.
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