Summary of Memcontrol: Mitigating Memorization in Diffusion Models Via Automated Parameter Selection, by Raman Dutt et al.
MemControl: Mitigating Memorization in Diffusion Models via Automated Parameter Selection
by Raman Dutt, Ondrej Bohdal, Pedro Sanchez, Sotirios A. Tsaftaris, Timothy Hospedales
First submitted to arxiv on: 29 May 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 proposes a solution to address the issue of data memorization in diffusion models, particularly in sensitive domains like medical imaging. The authors hypothesize that overparameterization is the root cause and introduce a bi-level optimization framework called MemControl to mitigate this problem. They show that regulating model capacity during fine-tuning can reduce memorization, but also require identifying specific parameter subsets for high-quality generation. Using MemControl, they discover parameter subsets that achieve a better tradeoff between quality and memorization. The approach outperforms existing strategies in medical image generation, requiring only 0.019% of model parameters to be fine-tuned. The framework is scalable, agnostic to reward functions, and can be integrated with other approaches for further mitigation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to solve a problem with computer models that generate images. These models are very good at making pictures look like the ones they learned from, but they also remember too much information about those original pictures. This is bad news because it could be used to identify people in medical images without their permission. The researchers think that this problem comes from the fact that these models have too many parameters (like a big toolbox). They propose a new way to fine-tune these models, called MemControl, which helps them generate better pictures while remembering less information. This approach works well for medical image generation and can even be used in other areas. The code is available online. |
Keywords
» Artificial intelligence » Diffusion » Fine tuning » Image generation » Optimization
