Summary of Distillation Of Discrete Diffusion Through Dimensional Correlations, by Satoshi Hayakawa et al.
Distillation of Discrete Diffusion through Dimensional Correlations
by Satoshi Hayakawa, Yuhta Takida, Masaaki Imaizumi, Hiromi Wakaki, Yuki Mitsufuji
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Numerical Analysis (math.NA); 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 This research paper proposes a new approach to discrete diffusion models, which are capable of capturing dependencies between elements while remaining scalable. The authors introduce “mixture” models that treat dimensional correlations, as well as a set of loss functions for distilling the iterations of existing models. The proposed method is shown to be effective in distilling pretrained discrete diffusion models across image and language domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Discrete diffusion models are designed to capture dependencies between elements in high-dimensional spaces. However, they can be slow due to their iterative nature. This paper proposes a new approach that captures these dependencies while remaining scalable. The authors introduce “mixture” models that treat dimensional correlations and provide a set of loss functions for distilling the iterations of existing models. |
Keywords
» Artificial intelligence » Diffusion
