Continued pretraining offers a promising solution for adapting foundation models to a new target domain. However, in specialized domains, available datasets are often very small, limiting the applicability of SSL methods developed for large-scale pretraining and making hyperparameter search infeasible. In addition, pretrained models are usually released as backbone-weights only, lacking important information to continue pretraining. We propose to bridge this gap with DIET-CP, a simple continued pretraining strategy, where any strong foundation model can be steered towards the new data distribution of interest. DIET-CP relies on a very simple objective, requires no labels, and introduces no more hyperparameters than supervised finetuning. It is stable across data modalities and backbone choices, while providing a significant performance boost for state-of-the-art models such as DINOv3 using only 1000 images.
@article{arxiv.2509.06990,
title = {DIET-CP: Lightweight and Data Efficient Self Supervised Continued Pretraining},
author = {Bryan Rodas and Natalie Montesino and Jakob Ambsdorf and David Klindt and Randall Balestriero},
journal= {arXiv preprint arXiv:2509.06990},
year = {2025}
}