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 this https URL

本文介绍了一种名为 CDGAN 的图像到图像转换网络，该网络在原有 CycleGAN 框架中添加了附加的鉴别器网络，以生成高质量、更逼真的图像，并在三个基准图像到图像转换数据集上进行了测试，结果表明该方法优于现有的最先进方法。