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.
CITATION STYLE
Okadome, Y., Wei, W., & Aizono, T. (2017). Parallel-pathway generator for generative adversarial networks to generate high-resolution natural images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10614 LNCS, pp. 655–662). Springer Verlag. https://doi.org/10.1007/978-3-319-68612-7_74
Mendeley helps you to discover research relevant for your work.