Summary of Dp-rdm: Adapting Diffusion Models to Private Domains Without Fine-tuning, by Jonathan Lebensold et al.
DP-RDM: Adapting Diffusion Models to Private Domains Without Fine-Tuning
by Jonathan Lebensold, Maziar Sanjabi, Pietro Astolfi, Adriana Romero-Soriano, Kamalika Chaudhuri, Mike Rabbat, Chuan Guo
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); 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 This research paper proposes a novel approach to text-to-image diffusion models that addresses the issue of sample-level memorization. The proposed algorithm, differentially private (DP) retrieval-augmented generation, provides provable privacy guarantees while generating high-quality image samples. The method assumes access to a pre-trained text-to-image model and uses a DP retrieval mechanism to augment the input prompt with retrieved images from a private dataset. This approach requires no fine-tuning on the private data and can generate high-quality images with state-of-the-art generative models, meeting rigorous privacy standards. In particular, the paper demonstrates its effectiveness on MS-COCO, achieving a 3.5-point improvement in FID over public-only retrieval for up to 10,000 queries while maintaining a privacy budget of ε=10. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to solve a problem with image generation models that memorize exact copies of training images. To fix this issue, the researchers developed a new method called differentially private (DP) retrieval-augmented generation. This approach helps keep generated images private while still creating high-quality pictures. The method uses an existing image model and adds retrieved images from a secret dataset to help generate new pictures. It’s a big improvement over previous methods that can create fake images quickly, but this one also keeps those images private. |
Keywords
* Artificial intelligence * Diffusion * Fine tuning * Image generation * Prompt * Retrieval augmented generation
