Data attribution methods trace model behavior back to its training dataset, offering an effective approach to better understand ``black-box'' neural networks. While prior research has established quantifiable links between model output and training data in diverse settings, interpreting diffusion model outputs in relation to training samples remains underexplored. In particular, diffusion models operate over a sequence of timesteps instead of instantaneous input-output relationships in previous contexts, posing a significant challenge to extend existing frameworks to diffusion models directly. Notably, we present Diffusion-TracIn that incorporates this temporal dynamics and observe that samples' loss gradient norms are highly dependent on timestep. This trend leads to a prominent bias in influence estimation, and is particularly noticeable for samples trained on large-norm-inducing timesteps, causing them to be generally influential. To mitigate this effect, we introduce Diffusion-ReTrac as a re-normalized adaptation that enables the retrieval of training samples more targeted to the test sample of interest, facilitating a localized measurement of influence and considerably more intuitive visualization. We demonstrate the efficacy of our approach through various evaluation metrics and auxiliary tasks, reducing the amount of generally influential samples to $\frac{1}{3}$ of its original quantity.

翻译：数据归因方法通过追溯模型行为至其训练数据集，为理解“黑箱”神经网络提供了有效途径。尽管已有研究在多种场景下建立了模型输出与训练数据之间的可量化联系，但将扩散模型输出与训练样本关联的解释仍鲜有探索。特别地，扩散模型的操作涉及一系列时间步长，而非传统意义上的瞬时输入-输出关系，这给将现有框架直接扩展至扩散模型带来了显著挑战。为此，我们提出融合时间动态特性的Diffusion-TracIn方法，并观察到样本损失梯度范数对时间步长高度敏感。该趋势导致因果效应估计出现显著偏差，尤其对于在大范数诱导时间步长上训练的样本更为突出，使其普遍表现为高影响力样本。为缓解这一效应，我们提出Diffusion-ReTrac方法作为重新归一化的改进方案，能够更针对性地检索与目标测试样本相关的训练数据，从而实现对影响的局部化度量，并显著提升可视化直观性。通过多项评估指标与辅助任务验证，本方法将普遍影响力样本的数量缩减至原始量的三分之一。