Summary of Synthetic Data Generation For System Identification: Leveraging Knowledge Transfer From Similar Systems, by Dario Piga et al.
Synthetic data generation for system identification: leveraging knowledge transfer from similar systems
by Dario Piga, Matteo Rufolo, Gabriele Maroni, Manas Mejari, Marco Forgione
First submitted to arxiv on: 8 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
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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 tackles the issue of overfitting when learning dynamical systems by proposing a novel method for generating synthetic data, which improves model generalization and robustness in scenarios with limited data. The approach involves knowledge transfer from similar systems to enhance model estimation. A pre-trained meta-model is used to generate synthetic output sequences based on new input sequences, while also serving as training data to define the loss function. The efficacy of this approach is demonstrated through a numerical example that highlights its advantages in system identification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn more about complex systems better by creating fake data to help our models make good predictions even when we don’t have much information. They do this by sharing knowledge between similar systems, which helps the model not get too specialized and overfit to a specific set of data. The authors show that using this fake data can actually improve how well their models work, especially when we’re dealing with small amounts of real data. |
Keywords
* Artificial intelligence * Generalization * Loss function * Overfitting * Synthetic data
