[2602.00114] 1S-DAug: One-Shot Data Augmentation for Robust Few-Shot Generalization

Computer Science > Computer Vision and Pattern Recognition arXiv:2602.00114 (cs) [Submitted on 27 Jan 2026 (v1), last revised 24 Mar 2026 (this version, v3)] Title:1S-DAug: One-Shot Data Augmentation for Robust Few-Shot Generalization Authors:Yunwei Bai, Ying Kiat Tan, Yao Shu, Tsuhan Chen View a PDF of the paper titled 1S-DAug: One-Shot Data Augmentation for Robust Few-Shot Generalization, by Yunwei Bai and 3 other authors View PDF HTML (experimental) Abstract:Few-shot learning (FSL) challenges model generalization to novel classes based on just a few shots of labeled examples, a testbed where traditional test-time augmentations fail to be effective. We introduce 1S-DAug, a one-shot generative augmentation operator that synthesizes diverse yet faithful variants from just one example image at test time. 1S-DAug couples traditional geometric perturbations with controlled noise injection and a denoising diffusion process conditioned on the original image. The generated images are then encoded and aggregated, alongside the original image, into a combined representation for more robust FSL predictions. Integrated as a training-free model-agnostic plugin, 1S-DAug consistently improves FSL across standard benchmarks of 4 different datasets without any model parameter update, including achieving up to 20% relative accuracy improvement on the miniImagenet 5-way-1-shot benchmark. Code will be released. Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligen...