Learning by competing: Competitive multi-generator based adversarial learning

Abstract

Generative adversarial networks (GANs) have been extensively used for dozens of image enhancement and image translation applications, where several traditional and novel architectures have been and are still being introduced. However, the classical training protocols do not fully explore the significant potential of such architectures. In this paper, we propose a novel conditional GAN (cGAN) framework called competitive multi-generator based cGAN (CMcGAN) for more effective training progress and enhanced image generation. Different from classical adversarial learning protocols, the generators of a CMcGAN are not only challenged by the discriminator for improved performance, but also by other generators. This is achieved by a sharing mechanism, where the current performances of the competing generators are shared among each other, motivating each generator to perform better than the rest. Extensive experiments in several conditional image generation applications show the significant improvements achieved by several traditional architectures when trained following our proposed learning strategy. Moreover, these architectures, trained by our learning strategy, outperformed the state-of-the-art approaches proposed in each tested application.