  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.
