Summary of Edge-preserving Noise For Diffusion Models, by Jente Vandersanden et al.
Edge-preserving noise for diffusion models
by Jente Vandersanden, Sascha Holl, Xingchang Huang, Gurprit Singh
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); 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 The paper proposes a novel edge-preserving diffusion model that generalizes denoising diffusion probabilistic models (DDPM). The authors introduce an edge-aware noise scheduler that varies between edge-preserving and isotropic Gaussian noise. This allows the generative process to converge faster to results that match the target distribution, enabling better representation of shapes and structural information in images. The model outperforms state-of-the-art baselines in unconditional image generation and is more robust for tasks guided by a shape-based prior. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to make images using computers. Right now, these programs can only make blurry or noisy versions of real pictures. The authors came up with an idea to add some extra features that help the program create sharper, more detailed images. This is important because it will let people use computer-generated images in all sorts of ways, like creating new characters for movies or making realistic-looking backgrounds for video games. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Image generation
