Parallel-Pathway Generator for Generative Adversarial Networks to Generate High-Resolution Natural Images

Yuya Okadome, Wenpeng Wei, Toshiko Aizono

Abstract: Generative Adversarial Networks (GANs) can learn various generative models such as probability distribution and images, while it is difficult to converge training. There are few successful methods for generating high-resolution images. In this paper, we propose the parallel-pathway generator network to generate high-resolution natural images. Our parallel network are constructed by parallelly stacked generators with different structure. To investigate the effect of our structure, we apply it to two image generation tasks: human-face image and road image which does not have square resolution. Results indicate that our method can generate high-resolution natural images with few parameter tuning.