Summary of Exploring the Potential Of Synthetic Data to Replace Real Data, by Hyungtae Lee and Yan Zhang and Heesung Kwon and Shuvra S. Bhattacharrya
Exploring the Potential of Synthetic Data to Replace Real Data
by Hyungtae Lee, Yan Zhang, Heesung Kwon, Shuvra S. Bhattacharrya
First submitted to arxiv on: 26 Aug 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 In this study, researchers investigate the potential of synthetic data to replace real data in AI applications. They find that using a small number of real images from domains other than the test domain, along with synthetic data, can improve model performance. The authors introduce two new metrics to evaluate the effectiveness of cross-domain training sets using synthetic data. By analyzing these metrics, they uncover factors influencing the potential of synthetic data and its impact on training performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Synthetic data is like fake news for AI – it’s a fake version of real data that can be used to train models instead of collecting more real data. In this study, scientists look at how using some real images along with fake ones affects the way models work. They come up with new ways to measure how well these fake datasets do in training models. By looking at these metrics, they figure out what makes synthetic data useful or not so much. |
Keywords
» Artificial intelligence » Synthetic data
