Summary of Constrained Diffusion with Trust Sampling, by William Huang et al.
Constrained Diffusion with Trust Sampling
by William Huang, Yifeng Jiang, Tom Van Wouwe, C. Karen Liu
First submitted to arxiv on: 17 Nov 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 The paper presents an optimization-based approach to address the limitations of diffusion models in generating constrained samples. By reformulating training-free loss-guided diffusion from an optimization perspective, the authors propose a series of constrained optimizations throughout the inference process. This involves allowing the sample to take multiple steps along the gradient of the proxy constraint function until the variance at each diffusion level indicates that the proxy is no longer trustworthy. Additionally, the paper estimates the state manifold of the diffusion model to enable early termination when the sample starts to deviate from it. The method, called trust sampling, balances between following the unconditional diffusion model and adhering to the loss guidance, resulting in more flexible and accurate constrained generation. Experimental results demonstrate significant improvements over existing methods on complex tasks such as image and 3D motion generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows a new way to make computer models generate specific things they shouldn’t normally do. This is useful for tasks like creating realistic images or motions that follow certain rules. The model uses an optimization method, which means it makes small changes until it gets what it wants. It also keeps track of where the generated data should be in order to avoid going too far off track. This results in better and more controlled generation compared to previous methods. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Inference » Optimization
