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Evolutionary Architectural Search for Generative Adversarial Networks

The Generative Adversarial Network (GAN) has shown powerfulness in various real-world artificial intelligence applications. However, its network architecture is generally designed through a manual trial-and-error process, which is relatively tedious, slow, and sub-optimal. This paper hence develops an evolutionary architectural search (EAS) technique to automate the entire design process of the GAN. In particular, different objective functions are used in the generator of a GAN as variation operators. This helps train the generator with various candidate architectures and their associated weights to play adversarial training against the discriminator of the GAN. Following evaluations by the discriminator, superior candidate generators survive to the next generation and evolve for potentially better architectures and their weights simultaneously, leading to more stabilized GANs with improved performance. The GAN designed through EAS is termed an EAS-GAN in this paper and is tested against existing evolutionary and other state-of-the-art GANs. The test results show that the EAS-GAN offers better generative performance overall, with the Fréchet inception distance scoring 22.1, 38.8, and 8.3 on the CIFAR-10, STL-10, and LSUN bedroom data sets, respectively.