[Submitted on 13 Oct 2026]
Title:Data Density Scaling Laws for Image Self-Distillation
View a PDF of the paper titled Data Density Scaling Laws for Image Self-Distillation, by Alvard Barseghyan and 3 other authors
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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.
Comments: 25 pages, 11 figures
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2610.19013 [cs.LG]
(or arXiv:2610.19013v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.19013
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Hrant Khachatrian [view email] [v1] Tue, 13 Oct 2026 18:08:51 UTC (9,817 KB)
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