Summary of Is Synthetic Image Useful For Transfer Learning? An Investigation Into Data Generation, Volume, and Utilization, by Yuhang Li et al.
Is Synthetic Image Useful for Transfer Learning? An Investigation into Data Generation, Volume, and Utilization
by Yuhang Li, Xin Dong, Chen Chen, Jingtao Li, Yuxin Wen, Michael Spranger, Lingjuan Lyu
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the generation and utilization of synthetic images derived from text-to-image generative models for facilitating transfer learning paradigms in deep learning. The authors observe that while the generated images have high visual fidelity, their naive incorporation into existing real-image datasets does not consistently enhance model performance due to a distribution gap between synthetic and real images. To address this issue, the paper introduces a novel two-stage framework called bridged transfer, which initially employs synthetic images for fine-tuning a pre-trained model and subsequently uses real data for rapid adaptation. The authors also propose dataset style inversion strategy to improve stylistic alignment between synthetic and real images. The proposed methods are evaluated across 10 different datasets and 5 distinct models, demonstrating consistent improvements with up to 30% accuracy increase on classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using fake images to help train deep learning models. These fake images are created by text-to-image generative models. While the fake images look real, they don’t always improve the model’s performance because there’s a gap between how they’re structured and how real images are structured. The authors came up with a new way to use these fake images called bridged transfer. They also proposed another method called dataset style inversion strategy to make sure the fake images match the structure of real images. They tested their methods on 10 different datasets and 5 different models, and they got better results. |
Keywords
» Artificial intelligence » Alignment » Classification » Deep learning » Fine tuning » Transfer learning
