Summary of Diffusion Attribution Score: Evaluating Training Data Influence in Diffusion Model, by Jinxu Lin et al.
Diffusion Attribution Score: Evaluating Training Data Influence in Diffusion Model
by Jinxu Lin, Linwei Tao, Minjing Dong, Chang Xu
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the pressing issue of misusing copyrighted and private images with diffusion models. To address this concern, researchers have proposed methods to attribute the contribution of specific training samples in generative models, known as data attribution. Existing approaches quantify this contribution by evaluating the change in diffusion loss when a sample is included or excluded from the training process. However, these methods are flawed because they measure divergence between predicted and ground truth distributions, which doesn’t accurately represent variance between model behaviors. To overcome this limitation, the authors introduce the Diffusion Attribution Score (DAS), which measures direct comparison between predicted distributions to analyze training sample importance. Theoretical analysis underpins DAS’s effectiveness, and strategies are explored to accelerate calculations for large-scale models. Experiments across various datasets and diffusion models demonstrate that DAS surpasses previous benchmarks in terms of linear data-modelling scores, setting a new state-of-the-art. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps fix a big problem with using images from the internet in AI models. Right now, people are misusing copyrighted and private images by training AI models on them. To stop this, researchers want to know how important each image is in making the AI model work. They’ve tried different methods to figure out which images matter most, but they haven’t been very good at it. The authors of this paper came up with a new way called DAS (Diffusion Attribution Score) that directly compares what the AI model predicts with what’s actually true. This helps us understand which images are really important and which ones aren’t. The paper shows that this new method is much better than previous methods at figuring out which images matter most. |
Keywords
» Artificial intelligence » Diffusion
