{"title":"HydraGAN: A Cooperative Agent Model for Multi-Objective Data Generation","authors":"Chance DeSmet, Diane J Cook","doi":"10.1145/3653982","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":48967,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology","volume":null,"pages":null},"PeriodicalIF":7.2000,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Intelligent Systems and Technology","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3653982","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
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.
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.