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, utilizing the feature space of a backbone pretrained via self-supervised learning to perform data attribution. 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.
@article{arxiv.2311.03386,
title = {A Simple and Efficient Baseline for Data Attribution on Images},
author = {Vasu Singla and Pedro Sandoval-Segura and Micah Goldblum and Jonas Geiping and Tom Goldstein},
journal= {arXiv preprint arXiv:2311.03386},
year = {2023}
}
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Code available at https://github.com/vasusingla/simple-data-attribution