Summary of Multi-scale Generative Modeling For Fast Sampling, by Xiongye Xiao et al.
Multi-scale Generative Modeling for Fast Sampling
by Xiongye Xiao, Shixuan Li, Luzhe Huang, Gengshuo Liu, Trung-Kien Nguyen, Yi Huang, Di Chang, Mykel J. Kochenderfer, Paul Bogdan
First submitted to arxiv on: 14 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
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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 This research proposes a novel approach to generative modeling in the wavelet domain, which tackles unique challenges such as sparse representation of high-frequency coefficients. By employing distinct strategies for low and high-frequency bands, the model improves performance, reduces trainable parameters, sampling steps, and time. The authors utilize score-based generative modeling with well-conditioned scores for low-frequency bands and multi-scale generative adversarial learning for high-frequency bands. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper develops a new way to create synthetic data that is more realistic and efficient by working in the wavelet domain instead of the traditional spatial domain. The approach addresses specific challenges like representing high-frequency details accurately. By using different techniques for low and high-frequency parts, the method outperforms existing methods while requiring less computational resources. |
Keywords
» Artificial intelligence » Synthetic data
