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Simple, Safe, and Overlooked: Reclaiming Sustainable Domain Generalization with Statistical Color Matching — arXiv:2608.18915

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.18915
License (as archived): CC0-1.0
Archived: 2026-10-18T13:53:53.524Z
SHA-256 of the archived text: ab6d9ea1143e63395ffaaf859564573fafd12726a4ebe5232bb51ffb2a6eea54
Stable URL of this page: https://metraipolis.com/documento/dc_b520800c8fd6

How to cite

Simple, Safe, and Overlooked: Reclaiming Sustainable Domain Generalization with Statistical Color Matching — arXiv:2608.18915 — arXiv (en), https://arxiv.org/abs/2608.18915 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_b520800c8fd6 (Metraipolis archive dc_b520800c8fd6, sha256 ab6d9ea1143e63395ffaaf859564573fafd12726a4ebe5232bb51ffb2a6eea54)

BibTeX

@misc{dcb520800c8fd6,
  title = {Simple, Safe, and Overlooked: Reclaiming Sustainable Domain Generalization with Statistical Color Matching — arXiv:2608.18915},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.18915},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_b520800c8fd6; sha256 ab6d9ea1143e63395ffaaf859564573fafd12726a4ebe5232bb51ffb2a6eea54},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.18915. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Hardware shifts, color variations, and changing patient characteristics between development and deployment routinely break trained medical image classifiers. Existing remedies fall short: standard color jittering provides insufficient diversity, while deep generative style transfer algorithms hallucinate features, destroy clinically relevant structures, and waste massive compute resources. To address this, we revisit classical statistical color matching and repurpose it as Colorist, a highly efficient data augmentation strategy that applies global mean-standard deviation matching directly in the RGB color space. We demonstrate that this training-free, fully interpretable approach safely generates structurally intact domain variations, outperforming deep generative models in structural fidelity and color alignment. Across out-of-distribution histopathology, peripheral blood, dermatology, and retinal datasets, it improves balanced accuracy by up to +9% over state-of-the-art domain generalization regularizers and by +13% over an unaugmented baseline. Moreover, by avoiding neural networks in the augmentation loop, Colorist preserves anatomical structure, minimizes carbon footprint, and integrates seamlessly into standard dataloaders. Together, these findings establish statistical matching as a safe, interpretable, yet overlooked alternative to deep architectures for clinical robustness. Source code is available at this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-20 in cs.LG).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Machine Learning (cs.LG); Image and Video Processing (eess.IV).
Pagina del lavoro: https://arxiv.org/abs/2608.18915 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T13:50:59.006Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-08-20?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).