  The primary motivation of Image-to-Image Transformation is to convert an
image of one domain to another domain. Most of the research has been focused on
the task of image transformation for a set of pre-defined domains. Very few
works are reported that actually developed a common framework for
image-to-image transformation for different domains. With the introduction of
Generative Adversarial Networks (GANs) as a general framework for the image
generation problem, there is a tremendous growth in the area of image-to-image
transformation. Most of the research focuses over the suitable objective
function for image-to-image transformation. In this paper, we propose a new
Cyclic-Synthesized Generative Adversarial Networks (CSGAN) for image-to-image
transformation. The proposed CSGAN uses a new objective function (loss) called
Cyclic-Synthesized Loss (CS) between the synthesized image of one domain and
cycled image of another domain. The performance of the proposed CSGAN is
evaluated on two benchmark image-to-image transformation datasets, including
CUHK Face dataset and CMP Facades dataset. The results are computed using the
widely used evaluation metrics such as MSE, SSIM, PSNR, and LPIPS. The
experimental results of the proposed CSGAN approach are compared with the
latest state-of-the-art approaches such as GAN, Pix2Pix, DualGAN, CycleGAN and
PS2GAN. The proposed CSGAN technique outperforms all the methods over CUHK
dataset and exhibits the promising and comparable performance over Facades
dataset in terms of both qualitative and quantitative measures. The code is
available at https://github.com/KishanKancharagunta/CSGAN.
