Summary of Diffusion Model Guided Sampling with Pixel-wise Aleatoric Uncertainty Estimation, by Michele De Vita et al.
Diffusion Model Guided Sampling with Pixel-Wise Aleatoric Uncertainty Estimation
by Michele De Vita, Vasileios Belagiannis
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Machine Learning (stat.ML)
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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 In this paper, researchers propose a novel approach to assess image quality in generative diffusion models by estimating pixel-wise aleatoric uncertainty during the sampling phase. This uncertainty is computed as the variance of denoising scores with a perturbation scheme specifically designed for diffusion models. The authors demonstrate that their uncertainty estimation algorithm and guided sampling method can filter out low-quality samples on ImageNet and CIFAR-10 datasets, achieving better FID scores. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps solve a problem in generative modeling by finding a way to measure how good the images are. They did this by looking at the “noise” or random parts of the pictures and calculating how much uncertainty is involved in creating those parts. This allowed them to get rid of low-quality images and make better ones. |
Keywords
» Artificial intelligence » Diffusion
