  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 paper,
motivated by the cooperative co-evolutionary algorithm, we propose a
Cooperative Dual Evolution based Generative Adversarial Network (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 multi-objective
optimization. Thus it exploits the complementary properties and injects dual
mutation diversity into training to steadily diversify the estimated density in
capturing multi-modes 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-Generator} and E-Discriminators), evolved by its own
evolutionary algorithm. Additionally, we further propose a Soft Mechanism to
balance the trade-off 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:
https://shiming-chen.github.io/CDE-GAN-website/CDE-GAN.html.
