Summary of Deep Diffusion Image Prior For Efficient Ood Adaptation in 3d Inverse Problems, by Hyungjin Chung et al.
Deep Diffusion Image Prior for Efficient OOD Adaptation in 3D Inverse Problems
by Hyungjin Chung, Jong Chul Ye
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 A recent paper proposes a novel deep diffusion image prior (DDIP) that leverages generative diffusion priors to solve inverse problems. Building upon the adaptation method of SCD, DDIP generalizes it by introducing a formal connection to the deep image prior. The proposed D3IP algorithm accelerates DDIP by orders of magnitude while achieving superior performance in 3D measurements. This enables seamless integration of 3D inverse solvers for coherent 3D reconstruction. Additionally, the paper shows that meta-learning techniques can be applied to further improve performance. Experimental results demonstrate the method’s ability to solve diverse 3D reconstructive tasks from a generative prior trained on phantom images vastly different from the training set. This opens up new opportunities for applying diffusion inverse solvers even when gold standard data is unavailable. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A recent paper improves how computers solve tricky problems by generating fake pictures that are similar to real ones. This helps them make better predictions and reconstruct 3D images more accurately. The authors show that their method can work with incomplete or fake training data, which makes it useful for real-world applications where perfect data is not available. This breakthrough has the potential to revolutionize how we use computers in fields like medicine, architecture, and engineering. |
Keywords
» Artificial intelligence » Diffusion » Meta learning
