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

TL;DR

1S-DAug is a test-time data augmentation method that synthesizes diverse images from a single example to improve few-shot learning accuracy without retraining.

Contribution

The paper introduces a novel test-time augmentation technique combining geometric and diffusion-based methods for improved few-shot classification.

Findings

Achieves up to 20% relative accuracy improvement on miniImageNet 5-way-1-shot.

Effective across four standard few-shot benchmarks.

Works without any model parameter updates.

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 few-shot predictions. Integrated as a training-free model-agnostic plugin, 1S-DAug consistently improves few-shot classification 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. Additionally, we provide extension experiments on the larger vision language models as well as theoretical analyses.