Summary of Local Curvature Smoothing with Stein’s Identity For Efficient Score Matching, by Genki Osada et al.
Local Curvature Smoothing with Stein’s Identity for Efficient Score Matching
by Genki Osada, Makoto Shing, Takashi Nishide
First submitted to arxiv on: 5 Dec 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 novel local curvature smoothing with Stein’s identity (LCSS) method is proposed to address the challenges in training score-based diffusion models (SDMs). The traditional score matching approach relies on computationally expensive Jacobian trace computation, which can be unstable and approximates learning as a denoising vector field rather than a true score. LCSS bypasses this issue by applying Stein’s identity, enabling regularization effectiveness and efficient computation. This method outperforms existing methods in sample generation performance and matches the performance of denoising score matching in evaluations such as FID, Inception score, and bits per dimension. Furthermore, LCSS enables realistic image generation even at high resolutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LCSS is a new way to train SDMs that makes it easier and faster. The old method was slow and didn’t work well because it needed to calculate something called the Jacobian trace. This new method uses Stein’s identity to avoid calculating this thing, making it more efficient and effective. LCSS does better than other methods in generating samples and is just as good as another popular method called denoising score matching. It even works well at very high resolutions. |
Keywords
» Artificial intelligence » Diffusion » Image generation » Regularization
