# Is Synthetic Image Useful for Transfer Learning? An Investigation into Data Generation, Volume, and Utilization

**Authors:** Yuhang Li, Xin Dong, Chen Chen, Jingtao Li, Yuxin Wen, Michael Spranger, Lingjuan Lyu  
**arXiv:** [2403.19866](https://arxiv.org/abs/2403.19866)  
**Date:** 2024-03-28  
**Summary page:** https://www.summarizepaper.com/en/paper/2403.19866v1/

## Key points

- Structured pipeline for leveraging synthetic data to enhance transfer learning
- Incorporating various general ImageNet pretrained models and fine-tuning the entire neural network
- Consideration of 10 downstream datasets from different domains
- Use of Stable Diffusion V1.5 for generating synthetic images
- Analysis of factors affecting image synthesis, including conducting primary experiments three times with different random seeds
- Synthetic data can be useful when strategically incorporated through bridged transfer frameworks and dataset style inversion strategies

## Summary

In this paper, we explore a structured pipeline for leveraging synthetic data to enhance transfer learning. Transfer learning involves fine-tuning a pre-trained model on general datasets to perform well on downstream domain-specific datasets. Our research expands the scope by incorporating various general ImageNet pretrained models and fine-tuning the entire neural network instead of just the classifier in a specific CLIP model as outlined by previous studies. We consider 10 downstream datasets from different domains and establish baseline performance using ResNet-18 before expanding to ResNet-50 and Vision Transformers. To generate synthetic images for training purposes, we employ Stable Diffusion V1.5 and utilize the ImageNet text prompt template tailored to specific class names from the datasets. Our study also includes an analysis of factors affecting image synthesis while highlighting that primary experiments were conducted three times with different random seeds. Overall findings suggest that synthetic data can be useful when incorporated strategically through bridged transfer frameworks and dataset style inversion strategies as proposed in this work; further research may explore expanding volumes of synthetic data for potentially greater improvements in model performance on classification tasks across various datasets and models evaluated in this study.