Summary of Regularized Distribution Matching Distillation For One-step Unpaired Image-to-image Translation, by Denis Rakitin et al.
Regularized Distribution Matching Distillation for One-step Unpaired Image-to-Image Translation
by Denis Rakitin, Ivan Shchekotov, Dmitry Vetrov
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 proposed Regularized Distribution Matching Distillation method modifies the original Distribution Matching Distillation framework for training one-step generators. This new approach focuses on unpaired image-to-image translation tasks, achieving competitive results compared to multi-step diffusion baselines. The method’s applicability is demonstrated through several 2D and dataset-based image translation examples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, scientists have developed a way to make computer models that generate images work more efficiently while keeping the same level of quality. This new approach helps machines create new pictures by translating ones they already know how to process. The method is shown to be effective in different scenarios where it matches or even outperforms other methods. |
Keywords
» Artificial intelligence » Diffusion » Distillation » Translation
