Multi-GANs and its application for Pseudo-Coloring

Generative Adversarial Networks (GANs) has shown its dramatical success, especially in computer vision applications. In this paper, inspired by traditional GANs, we propose Multi-GANs which is an architecture of multiple generative adversarial networks that works together. Whilst, the GANs are successful to generate images which looks realistic but the real-world problems are much more complicated than a GANs can perform a desirable outcome to the whole of the problem space. Therefore, our approach divides each problem space into the several smaller and of course much more homogeneous subspaces. We propose then a GANs for each sub-space that can learn to mimic any distribution of data with lower lost. The results of each GANs for all sub-spaces then merge together to perform the original preliminary space. We evaluated our approach on Pseudo-Coloring which is a very difficult and ill-posed problem among the computer vision community. The experimental results show much more realistic characteristics for the generated images also its superiority in comparison to the traditional approaches.