DSF-GAN: DownStream Feedback Generative Adversarial Network

• URL: http://arxiv.org/abs/2403.18267v1
• Date: Wed, 27 Mar 2024 05:41:50 GMT
• Title: DSF-GAN: DownStream Feedback Generative Adversarial Network
• Authors: Oriel Perets, Nadav Rappoport,
• Abstract summary: We propose a novel architecture called the DownStream Feedback Generative Adversarial Network (DSF-GAN) DSF-GAN incorporates feedback from a downstream prediction model during training to augment the generator's loss function with valuable information. Our experiments demonstrate improved model performance when training on synthetic samples generated by DSF-GAN, compared to those generated by the same GAN architecture without feedback.
• Score: 0.07083082555458872
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Utility and privacy are two crucial measurements of the quality of synthetic tabular data. While significant advancements have been made in privacy measures, generating synthetic samples with high utility remains challenging. To enhance the utility of synthetic samples, we propose a novel architecture called the DownStream Feedback Generative Adversarial Network (DSF-GAN). This approach incorporates feedback from a downstream prediction model during training to augment the generator's loss function with valuable information. Thus, DSF-GAN utilizes a downstream prediction task to enhance the utility of synthetic samples. To evaluate our method, we tested it using two popular datasets. Our experiments demonstrate improved model performance when training on synthetic samples generated by DSF-GAN, compared to those generated by the same GAN architecture without feedback. The evaluation was conducted on the same validation set comprising real samples. All code and datasets used in this research will be made openly available for ease of reproduction.

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