Summary of Dsf-gan: Downstream Feedback Generative Adversarial Network, by Oriel Perets et al.
DSF-GAN: DownStream Feedback Generative Adversarial Network
by Oriel Perets, Nadav Rappoport
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed DownStream Feedback Generative Adversarial Network (DSF-GAN) aims to enhance the utility of synthetic tabular data by incorporating feedback from a downstream prediction model during training. This approach modifies the generator’s loss function with valuable information, allowing it to generate more accurate and useful synthetic samples. The DSF-GAN outperforms its GAN counterpart without feedback in various experiments using popular datasets. By leveraging a downstream prediction task, this method demonstrates improved model performance when trained on synthetic samples. The availability of code and datasets will facilitate replication of the results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Synthetic data is important for machine learning because it helps keep our personal information private. Right now, making fake data that’s useful and accurate is tricky. A new way to do this called DSF-GAN tries to make better synthetic data by listening to what a downstream model thinks about the data. This helps the fake data be more like real data. The scientists tested DSF-GAN on two big datasets and found it did a lot better than the old way of making fake data. |
Keywords
* Artificial intelligence * Gan * Generative adversarial network * Loss function * Machine learning * Synthetic data
