Multiobjective coevolutionary training of Generative Adversarial Networks

被引：1

作者：

Ripa, Guillermo ^{[1
]}

Mautone, Agustin ^{[1
]}

Vidal, Andres ^{[1
]}

Nesmachnow, Sergio ^{[1
]}

Toutouh, Jamal ^{[2
]}

机构：

[1] Univ Republica, Montevideo, Uruguay

[2] Univ Malaga, ITIS Software, Malaga, Spain

来源：

PROCEEDINGS OF THE 2023 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, GECCO 2023 COMPANION | 2023年

基金：

欧盟地平线“2020”;

关键词：

Generative Adversarial Networks; multiobjective optimization; co-evolutionary algorithms; image generation;

D O I：

10.1145/3583133.3590626

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This article presents a multiobjective evolutionary approach for coevolutionary training of Generative Adversarial Networks. The proposal applies an explicit multiobjective optimization approach based on Pareto ranking and non-dominated sorting over the co-evolutionary search implemented by the Lipizzaner framework, to optimize the quality and diversity of the generated synthetic data. Two functions are studied for evaluating diversity. The main results obtained for the handwritten digits generation problem show that the proposed multiobjective search is able to compute accurate and diverse solutions, improving over the standard Lipizzaner implementation.

引用

页码：319 / 322

页数：4

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