论文标题
CDGAN：用于图像到图像转换的环状判别生成对抗网络
CDGAN: Cyclic Discriminative Generative Adversarial Networks for Image-to-Image Transformation
论文作者
论文摘要
生成对抗网络（GAN）已促进了解决图像到图像转换问题的新方向。不同的gan使用目标函数中具有不同损失的生成器和歧视网络。仍然存在一个差距，既可以填补生成的图像的质量和接近地面真相图像。在这项工作中，我们引入了一个新的图像到图像转换网络，名为环状生成对抗网络（CDGAN），该网络填补了上述差距。提出的CDGAN除了在Cyclegan的原始体系结构之外，还将循环图像的其他鉴别网络合并为循环图像，从而生成了高质量和更现实的图像。在三个图像到图像转换数据集上测试了提出的CDGAN。分析定量和定性结果，并将其与最新方法进行比较。在比较三个基线图像到图像转换数据集时，提出的CDGAN方法比最新方法优于最先进的方法。该代码可在https://github.com/kishankancharagunta/cdgan上找到。
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.
