Summary of Leveraging Programmatically Generated Synthetic Data For Differentially Private Diffusion Training, by Yujin Choi et al.
Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training
by Yujin Choi, Jinseong Park, Junyoung Byun, Jaewook Lee
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 DP-SynGen is a novel approach for generating realistic images in differential private training. By leveraging programmatically generated synthetic data in diffusion models, DP-SynGen addresses the challenge of adapting synthetic data for generative models. The authors identify three stages in diffusion models – coarse, context, and cleaning – where synthetic data can be effectively utilized. They show that the cleaning and coarse stages can be trained without private data, reducing the privacy budget. Experimental results demonstrate that DP-SynGen improves the quality of generated data by mitigating noise caused by privacy-induced noise. This approach has implications for applications such as image generation in medical imaging and facial recognition. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a way to generate realistic images without actually taking pictures or using real data. That’s what DP-SynGen does! It uses special computer algorithms to create fake images that are almost indistinguishable from real ones. This helps keep people’s private information safe while still allowing us to use computers to make new and exciting things, like better medical imaging tools or more accurate facial recognition software. |
Keywords
» Artificial intelligence » Image generation » Synthetic data
