  Generative Adversarial Networks (GANs) have facilitated a new direction to
tackle the image-to-image transformation problem. Different GANs use generator
and discriminator networks with different losses in the objective function.
Still there is a gap to fill in terms of both the quality of the generated
images and close to the ground truth images. In this work, we introduce a new
Image-to-Image Transformation network named Cyclic Discriminative Generative
Adversarial Networks (CDGAN) that fills the above mentioned gaps. The proposed
CDGAN generates high quality and more realistic images by incorporating the
additional discriminator networks for cycled images in addition to the original
architecture of the CycleGAN. The proposed CDGAN is tested over three
image-to-image transformation datasets. The quantitative and qualitative
results are analyzed and compared with the state-of-the-art methods. The
proposed CDGAN method outperforms the state-of-the-art methods when compared
over the three baseline Image-to-Image transformation datasets. The code is
available at https://github.com/KishanKancharagunta/CDGAN.
