Influence Functions for Scalable Data Attribution in Diffusion Models

2024 article mlodozeniec2024influencediffusion ○ Not yet verified

Authors

Bruno Mlodozeniec, Runa Eschenhagen, Juhan Bae, Alexander Immer, David Krueger, Richard Turner

Venue

arXiv preprint

Abstract

Develops influence function frameworks for diffusion models to address data attribution and interpretability challenges. Predicts how model output would change if training data were removed, showing how previously proposed methods can be interpreted as particular design choices in this framework.

BibTeX

Local Entry

@article{mlodozeniec2024influencediffusion,
  title = {Influence Functions for Scalable Data Attribution in Diffusion Models},
  author = {Bruno Mlodozeniec and Runa Eschenhagen and Juhan Bae and Alexander Immer and David Krueger and Richard Turner},
  year = {2024},
  journal = {arXiv preprint},
  url = {https://arxiv.org/abs/2410.13850},
  abstract = {Develops influence function frameworks for diffusion models to address data attribution and interpretability challenges. Predicts how model output would change if training data were removed, showing how previously proposed methods can be interpreted as particular design choices in this framework.}
}

From AUTO:OPENALEX

@article{mlodozeniec2024influencediffusion,
  title = {Influence Functions for Scalable Data Attribution in Diffusion Models},
  author = {Bruno Mlodozeniec and Runa Eschenhagen and Juhan Bae and Alexander Immer and David Krueger and Richard E. Turner},
  year = {2024},
  doi = {10.32388/bojdxm}
}