DSF-GAN: DownStream Feedback Generative Adversarial Network
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.
Code (1)
Tasks
Generative Adversarial NetworkMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Feedback Adversarial Learning: Spatial Feedback for Improving Generative Adversarial Networks
We propose feedback adversarial learning (FAL) framework that can improve existing generative adversarial networks by leveraging spatial feedback from the discriminator. We formulate the generation task as a recurrent fr…
Image GenerationImage-to-Image TranslationTranslationNeural Networks with Recurrent Generative Feedback
Neural networks are vulnerable to input perturbations such as additive noise and adversarial attacks. In contrast, human perception is much more robust to such perturbations. The Bayesian brain hypothesis states that hum…
Adversarial RobustnessGenerative Market Equilibrium Models with Stable Adversarial Learning via Reinforcement
We present a general computational framework for solving continuous-time financial market equilibria under minimal modeling assumptions while incorporating realistic financial frictions, such as trading costs, and suppor…
Deep Reinforcement LearningNearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback
Learning from human feedback plays an important role in aligning generative models, such as large language models (LLM). However, the effectiveness of this approach can be influenced by adversaries, who may intentionally…
Ensemble Generative Cleaning with Feedback Loops for Defending Adversarial Attacks
Effective defense of deep neural networks against adversarial attacks remains a challenging problem, especially under powerful white-box attacks. In this paper, we develop a new method called ensemble generative cleaning…