Summary of A Label Is Worth a Thousand Images in Dataset Distillation, by Tian Qin et al.
A Label is Worth a Thousand Images in Dataset Distillation
by Tian Qin, Zhiwei Deng, David Alvarez-Melis
First submitted to arxiv on: 15 Jun 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 Data quality is a crucial factor in machine learning model performance, and dataset distillation methods aim to compress training datasets into smaller counterparts that maintain similar downstream performance. Understanding how these methods work is vital for improving them and revealing fundamental characteristics of “good” training data. This paper studies the role of soft (probabilistic) labels in state-of-the-art distillation methods, highlighting their significance in achieving high-performance models. Soft labels are used in most distillation approaches, and our ablation experiments reveal that their use is the main factor explaining performance gains, rather than specific techniques for generating synthetic data. We also find that not all soft labels are created equal, requiring structured information to be beneficial. Our findings challenge conventional wisdom, underscore the importance of soft labels in learning, and suggest new directions for improving distillation methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how machine learning models can be improved by using a technique called dataset distillation. Distillation takes big training datasets and compresses them into smaller ones that still work well. The question is, what makes this process work? The answer lies in something called soft labels. Soft labels are like hints that help the model learn better. This paper shows that soft labels are the key to making distillation work well, rather than the specific way they’re created. It also finds that not all soft labels are equal – some are better than others. |
Keywords
» Artificial intelligence » Distillation » Machine learning » Synthetic data
