Data Density Scaling Laws for Image Self-Distillation

Abstract:Current scaling law formulations suggest that increasing the number of unique samples in a dataset improves model performance, while repeated exposure to the same samples provides diminishing value. In this work, we examine this assumption for image self-distillation models. Through systematic experiments across diverse datasets, namely Maxar, SATLAS, Waymo, nuPlan, Ego4D, ImageNet, and YFCC15M, we demonstrate that the effects of sample repetition on downstream performance are non-trivial and dataset-dependent. We identify settings where repeatedly training on a smaller random subset can outperform training on a larger set of unique samples. This finding highlights a limitation of existing scaling laws. To address this gap, we propose a modified version of a Chinchilla-style scaling law that explicitly accounts for two factors: the number of unique samples and repetition per sample. This formulation provides a framework for understanding data-compute trade-offs and improves prediction in some regimes where repetition plays a central role, although its benefits are not consistent across datasets. Finally, we examine how subset selection and augmentation affect downstream performance.

Machine Learning (cs.LG)

arXiv:2610.19013 [cs.LG]

(or arXiv:2610.19013v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.19013 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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