Data-Efficient Classifier Pretraining Through Diffusion Models

Qinyu Li; Yee Whye Teh; Chris Holmes; Joel Jaskari

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arXiv · 2610.23289

Data-Efficient Classifier Pretraining Through Diffusion Models

Qinyu Li·Yee Whye Teh·Chris Holmes·Joel Jaskari

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Abstract

Machine learning with limited data is susceptible to overfitting, especially with modern deep neural networks. Prior to the introduction of AlexNet, reconstruction-based pretraining with auto-encoder and denoising auto-encoder were found to be beneficial, partially due to the limited size of the training sets at the time. Motivated by these traditional approaches, we propose DiffPT, a simple diffusion modeling approach to pretrain a given classifier backbone for limited domain-specific training data. We experiment on time-series data, standard natural-image benchmarks, and medical imaging datasets, and contrast the diffusion approach to a multitude of modern and traditional un/self-supervised methods. While contrastive pretraining performs better on CIFAR-10 and Tiny ImageNet in low-data regimes, DiffPT is particularly effective on medical imaging and time-series tasks, where it outperforms self-supervised baselines across a range of low-data regimes. Our results suggest that DiffPT is a simple and effective approach for data-efficient classifier pretraining on different modalities.

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BibTeX RIS

Qinyu Li, Yee Whye Teh, Chris Holmes, Joel Jaskari. 2026-10-16. Data-Efficient Classifier Pretraining Through Diffusion Models. https://arxiv.org/abs/2610.23289

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