@inproceedings{
anonymous2023a,
title={A Simple and Efficient Baseline for Data Attribution on Images},
author={Anonymous},
booktitle={NeurIPS Workshop on Attributing Model Behavior at Scale},
year={2023},
url={https://openreview.net/forum?id=WGjQt3aDn7}
}
Data attribution methods play a crucial role in understanding machine learning models, providing insight into which training data points are most responsible for model outputs during deployment. However, current state-of-the-art approaches require a large ensemble of as many as 300,000 models to accurately attribute model predictions. These approaches therefore come at a high computational cost, are memory intensive, and are hard to scale to large models or datasets. 
In this work, we focus on a minimalist baseline that relies on the image features from a pretrained self-supervised backbone to retrieve images from the dataset. Our method is model-agnostic and scales easily to large datasets. We show results on CIFAR-10 and ImageNet, achieving strong performance that rivals or outperforms state-of-the-art approaches at a fraction of the compute or memory cost. Contrary to prior work, our results reinforce the intuition that a model's prediction on one image is most impacted by visually similar training samples. Our approach serves as a simple and efficient baseline for data attribution on images.
A Simple and Efficient Baseline for Data Attribution on Images