We present LatentDiff, a scalable framework for semantic dataset comparison that operates directly in the latent space of pretrained vision encoders. By combining sparse autoencoder-based divergence testing with density ratio estimation, LatentDiff identifies interpretable semantic differences between datasets at a fraction of the computational cost of caption-based alternatives. We also introduce Noisy-Diff, a benchmark capturing realistic sparse distribution shifts that cause existing methods to struggle. Experiments demonstrate that LatentDiff achieves superior accuracy while remaining robust to settings where an extremely small fraction of images (from 5% to <1% ) differ semantically.
@article{arxiv.2605.00899,
title = {LatentDiff: Scaling Semantic Dataset Comparison to Millions of Images},
author = {James Flora and Kowshik Thopalli and Akshay R. Kulkarni and Weng-Keen Wong and Shusen Liu},
journal= {arXiv preprint arXiv:2605.00899},
year = {2026}
}