Synthetic image data generation represents a promising avenue for training
deep learning models, particularly in the realm of transfer learning, where
obtaining real images within a specific domain can be prohibitively expensive
due to privacy and intellectual property considerations. This work delves into
the generation and utilization of synthetic images derived from text-to-image
generative models in facilitating transfer learning paradigms. Despite the high
visual fidelity of the generated images, we observe that their naive
incorporation into existing real-image datasets does not consistently enhance
model performance due to the inherent distribution gap between synthetic and
real images. To address this issue, we introduce a novel two-stage framework
called bridged transfer, which initially employs synthetic images for
fine-tuning a pre-trained model to improve its transferability and subsequently
uses real data for rapid adaptation. Alongside, We propose dataset style
inversion strategy to improve the stylistic alignment between synthetic and
real images. Our proposed methods are evaluated across 10 different datasets
and 5 distinct models, demonstrating consistent improvements, with up to 30%
accuracy increase on classification tasks. Intriguingly, we note that the
enhancements were not yet saturated, indicating that the benefits may further
increase with an expanded volume of synthetic data.

通过合成图像数据生成实现的迁移学习模型训练为主题的研究中，我们提出了一种新的两阶段框架，称为桥接传递，通过使用合成图像进行模型微调从而提高其迁移能力，随后使用真实数据进行快速适应。同时，我们提出了数据集风格反转策略来提高合成图像与真实图像之间的风格对齐。通过在 10 个不同数据集和 5 个不同模型上的评估，我们展示了一致的改进效果，在分类任务上的准确率提高高达 30%。有趣的是，我们注意到改进还未饱和，这表明随着合成数据量的增加，还可能进一步提高收益。