Summary of Similarity Trajectories: Linking Sampling Process to Artifacts in Diffusion-generated Images, by Dennis Menn et al.
Similarity Trajectories: Linking Sampling Process to Artifacts in Diffusion-Generated Images
by Dennis Menn, Feng Liang, Hung-Yueh Chiang, Diana Marculescu
First submitted to arxiv on: 22 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposed paper introduces a novel approach to detect artifacts in diffusion model-generated images, which is crucial for correcting their output. The method leverages the similarity of denoised images at consecutive time steps during the sampling process to identify the severity of artifacts. By characterizing this similarity trajectory, the authors demonstrate that it correlates with the presence of artifacts. A classifier trained on these trajectories achieves an impressive accuracy of 72.35% using a limited annotated dataset of only 680 images, compared to the millions of images required by existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps fix mistakes in computer-generated pictures. It finds a new way to look at how images change during the process of creating them. By comparing these changes, we can tell if an image has mistakes or not. This method uses very little training data compared to other approaches and gets results that are almost as good. |
Keywords
* Artificial intelligence * Diffusion model
