Summary of What Is Dataset Distillation Learning?, by William Yang et al.
What is Dataset Distillation Learning?
by William Yang, Ye Zhu, Zhiwei Deng, Olga Russakovsky
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper investigates dataset distillation, a technique that creates synthetic data to help train high-performing models. By analyzing distilled data, researchers reveal that it cannot replace real data during training, but it can retain task performance by compressing information from early model training dynamics. The study also provides an interpretation framework for distilled data and shows that individual points contain meaningful semantic information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Dataset distillation creates synthetic data to help train models. This paper looks at what’s stored in this distilled data and how it works. It finds that the data can’t replace real data, but it keeps task performance by keeping info from early model training. The study also shows a way to understand this distilled data better and says each point has meaningful information. |
Keywords
» Artificial intelligence » Distillation » Synthetic data
