Data attribution seeks to trace model outputs back to training data. With the
recent development of diffusion models, data attribution has become a desired
module to properly assign valuations for high-quality or copyrighted training
samples, ensuring that data contributors are fairly compensated or credited.
Several theoretically motivated methods have been proposed to implement data
attribution, in an effort to improve the trade-off between computational
scalability and effectiveness. In this work, we conduct extensive experiments
and ablation studies on attributing diffusion models, specifically focusing on
DDPMs trained on CIFAR-10 and CelebA, as well as a Stable Diffusion model
LoRA-finetuned on ArtBench. Intriguingly, we report counter-intuitive
observations that theoretically unjustified design choices for attribution
empirically outperform previous baselines by a large margin, in terms of both
linear datamodeling score and counterfactual evaluation. Our work presents a
significantly more efficient approach for attributing diffusion models, while
the unexpected findings suggest that at least in non-convex settings,
constructions guided by theoretical assumptions may lead to inferior
attribution performance. The code is available at
this https URL

数据归因通过追溯模型输出至训练数据，以便为高质量或受版权保护的训练样本进行正确的归属评估，确保数据贡献者得到公正的补偿或认可。本研究在扩散模型上进行了广泛的实验和消融研究，特别关注 DDPMs 在 CIFAR-10、CelebA 以及在 ArtBench 上经过 LoRA 微调的稳定扩散模型的归因问题。有趣的是，我们报告了一些违背直觉的观察结果，即在理论上没有依据的设计选择在实践中表现出比以往基准线更好的性能，无论是线性数据建模得分还是对事实的评估。我们提出了一种更高效的方法来为扩散模型进行归因，而意外的发现表明，至少在非凸设置中，受到理论假设指导的构建可能导致较差的归因性能。该文提供了代码链接供参考。