HydraGAN:多目标数据生成的合作代理模型

IF 7.2 4区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Chance DeSmet, Diane J Cook
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引用次数: 0

摘要

生成对抗网络已成为生成与真实数据相似的合成数据点的一种事实上的方法。我们要解决的问题是,单个样本的真实性并不是生成合成数据的唯一标准。隐私保护、分布真实性和多样性促进等其他约束条件也可能是优化的必要条件。为了应对这一挑战,我们引入了 HydraGAN,这是一个可执行多目标合成数据生成的多代理网络。我们从理论上验证了对 HydraGAN 系统(包含单个生成器和任意数量的判别器)的训练会导致纳什均衡。六个数据集的实验结果表明,HydraGAN 在最大化雷达曲线下面积 (AuRC) 方面始终优于之前的方法,同时兼顾了合作或竞争数据生成目标的组合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
HydraGAN: A Cooperative Agent Model for Multi-Objective Data Generation

Generative adversarial networks have become a de facto approach to generate synthetic data points that resemble their real counterparts. We tackle the situation where the realism of individual samples is not the sole criterion for synthetic data generation. Additional constraints such as privacy preservation, distribution realism, and diversity promotion may also be essential to optimize. To address this challenge, we introduce HydraGAN, a multi-agent network that performs multi-objective synthetic data generation. We theoretically verify that training the HydraGAN system, containing a single generator and an arbitrary number of discriminators, leads to a Nash equilibrium. Experimental results for six datasets indicate that HydraGAN consistently outperforms prior methods in maximizing the Area under the Radar Curve (AuRC), balancing a combination of cooperative or competitive data generation goals.

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来源期刊
ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.30
自引率
2.00%
发文量
131
期刊介绍: ACM Transactions on Intelligent Systems and Technology is a scholarly journal that publishes the highest quality papers on intelligent systems, applicable algorithms and technology with a multi-disciplinary perspective. An intelligent system is one that uses artificial intelligence (AI) techniques to offer important services (e.g., as a component of a larger system) to allow integrated systems to perceive, reason, learn, and act intelligently in the real world. ACM TIST is published quarterly (six issues a year). Each issue has 8-11 regular papers, with around 20 published journal pages or 10,000 words per paper. Additional references, proofs, graphs or detailed experiment results can be submitted as a separate appendix, while excessively lengthy papers will be rejected automatically. Authors can include online-only appendices for additional content of their published papers and are encouraged to share their code and/or data with other readers.
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