- Out of the listed baselines, to the best of my knowledge only Journey TRAK [1] has been explicitly used for diffusion models in previous work. As the authors note, Journey TRAK is not meant to be used to attribute the *final* image $x$ (i.e., the entire sampling trajectory). Rather, it is meant to attribute noisy images $x_t$ (i.e., specific denoising steps along the sampling trajectory). Thus, the direct comparison with Journey TRAK in the evaluation section is not on equal grounds.
- For the counterfactual experiments, I would have liked to see a comparison against Journey TRAK [1] used at a particular step of the the sampling trajectory. In particular, [1, Figure 2] shows a much larger effect of removing high-scoring images according to Journey TRAK, in comparison with CLIP cosine similarity.
- Given that the proposed method is only a minor modification of existing methods [1, 2], I would have appreciated a more thorough attempt at explaining/justifying the changes proposed by the authors.
[1] Kristian Georgiev, Joshua Vendrow, Hadi Salman, Sung Min Park, and Aleksander Madry. The
journey, not the destination: How data guides diffusion models. In Workshop on Challenges in
Deployable Generative AI at International Conference on Machine Learning (ICML), 2023.
[2] Sung Min Park, Kristian Georgiev, Andrew Ilyas, Guillaume Leclerc, and Aleksander Madry. Trak:
Attributing model behavior at scale. In International Conference on Machine Learning (ICML), 2023.
- For the counterfactual experiments, I would have liked to see a comparison against Journey TRAK [1] used at a particular step of the the sampling trajectory. In particular, [1, Figure 2] shows a much larger effect of removing high-scoring images according to Journey TRAK, in comparison with CLIP cosine similarity.