Summary of On the Equivalency, Substitutability, and Flexibility Of Synthetic Data, by Che-jui Chang et al.
On the Equivalency, Substitutability, and Flexibility of Synthetic Data
by Che-Jui Chang, Danrui Li, Seonghyeon Moon, Mubbasir Kapadia
First submitted to arxiv on: 24 Mar 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 The paper investigates the effectiveness of using synthetic data to train perception models in real-world scenarios. Leveraging synthetic data has become a popular strategy due to its efficiency, scalability, perfect annotations, and low costs. Despite its proven advantages, few studies have focused on how to efficiently generate synthetic datasets to solve real-world problems and what extent synthetic data can reduce the effort for real-world data collection. To answer these questions, the authors systematically investigate several properties of synthetic data, including its equivalency to real-world data, substitutability for real data, and flexibility in closing domain gaps. The authors use the M3Act synthetic data generator and conduct experiments on DanceTrack and MOT17 datasets. The results show that synthetic data not only enhances model performance but also demonstrates substitutability for real data, with 60% to 80% replacement without performance loss. Additionally, the study highlights the importance of flexible data generators in narrowing domain gaps for improved model adaptability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Using synthetic data to train perception models can be a game-changer! Scientists are trying to figure out how well it works and if it’s really necessary to collect real-world data. They looked at some cool properties like how synthetic data compares to real data, if it can replace real data, and how flexible the generators are. They used a special tool called M3Act and tested it on two datasets. The results show that synthetic data is pretty good and can even replace 60% to 80% of real-world data without losing performance! This means we might not need as much real-world data in the future, which could save us time and money. |
Keywords
* Artificial intelligence * Synthetic data
