University of Tasmania

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CDE-GAN: Cooperative dual evolution based generative adversarial network

journal contribution
posted on 2023-05-20, 20:56 authored by Chen, S, Wang, W, Xia, B, You, X, Peng, Q, Cao, Z, Ding, W
Generative adversarial networks (GANs) have been a popular deep generative model for real-world applications. Despite many recent efforts on GANs that have been contributed, mode collapse and instability of GANs are still open problems caused by their adversarial optimization difficulties. In this article, motivated by the cooperative co-evolutionary algorithm, we propose a cooperative dual evolution-based GAN (CDE-GAN) to circumvent these drawbacks. In essence, CDE-GAN incorporates dual evolution with respect to the generator(s) and discriminators into a unified evolutionary adversarial framework to conduct effective adversarial multiobjective optimization. Thus, it exploits the complementary properties and injects dual mutation diversity into the training, to steadily diversify the estimated density in capturing multimodes and improve generative performance. Specifically, CDE-GAN decomposes the complex adversarial optimization problem into two subproblems (generation and discrimination), and each subproblem is solved with a separated subpopulation ( E-Generators and E-Discriminators ), evolved by its own evolutionary algorithm. Additionally, we further propose a Soft Mechanism to balance the tradeoff between E-Generators and E-Discriminators to conduct steady training for CDE-GAN. Extensive experiments on one synthetic dataset and three real-world benchmark image datasets demonstrate that the proposed CDE-GAN achieves a competitive and superior performance in generating good quality and diverse samples over baselines. The code and more generated results are available at our project homepage .


Publication title

Ieee Transactions on Evolutionary Computation






986 - 1000




School of Information and Communication Technology


Ieee-Inst Electrical Electronics Engineers Inc

Place of publication

445 Hoes Lane, Piscataway, USA, Nj, 08855

Rights statement

(c) 2021 IEEE

Repository Status

  • Restricted

Socio-economic Objectives

Artificial intelligence