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arXiv · 2602.00114

Can One-Shot Test-Time Data Augmentation Help with Generalization?

Yunwei Bai·Yao Shu·Ying Kiat Tan·Tsuhan Chen
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Abstract

Data augmentation is crucial for model generalization, but existing methods are mostly centered on the training stage. Test-time augmentation, while underexplored, can be practically effective for generalization while avoiding extra model parameters or fine-tuning. Given the increasing training cost and the literature gap, we study whether it is possible to perform effective test-time augmentation using image generation from just the single original image. We first analyze the importance of test-time augmentation, and then design and study a simple yet natural operator named 1S-DAug, which comprises geometric perturbations with controlled noise injection and image-conditioned denoising. We obtain positive results on well-established image-classification benchmarks across four datasets and multiple models, achieving up to 20\% relative accuracy improvement without model training or parameter access. Code will be released.

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Yunwei Bai, Yao Shu, Ying Kiat Tan, Tsuhan Chen. 2026-09-05. Can One-Shot Test-Time Data Augmentation Help with Generalization?. https://arxiv.org/abs/2602.00114

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