  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.
