论文标题
Relgan：GAN的一致性具有不相交的限制和对多个转换学习的生成过程的相对学习
ReLGAN: Generalization of Consistency for GAN with Disjoint Constraints and Relative Learning of Generative Processes for Multiple Transformation Learning
论文作者
论文摘要
图像转化到图像转化因其对包括医疗在内的不同应用的巨大影响，从不同的研究社区中获得了知名度。在这项工作中，我们引入了一种通用方案，以使GAN体系结构具有两个新的转换学习概念（TL）和相对学习概念（RER），以增强学习图像转换。 GAN体系结构的一致性遭受了限制不足，并且未能学习多种模式转换，这对于许多医疗应用是不可避免的。主要缺点是它专注于创建中间且可行的混合动力车，这对于关注分钟细节的医疗应用程序不允许。另一个缺点是两个学习阶段与TL和REL之间的相互关系较弱，在其中提出了改进的协调。我们已经在公共数据集上展示了新型网络框架的功能。我们强调，我们的新颖体系结构为图像提供了改进的神经图像转换版本，这对医学界来说是可以接受的。与以前的作品相比，实验和结果证明了我们框架和增强框架的有效性。
Image to image transformation has gained popularity from different research communities due to its enormous impact on different applications, including medical. In this work, we have introduced a generalized scheme for consistency for GAN architectures with two new concepts of Transformation Learning (TL) and Relative Learning (ReL) for enhanced learning image transformations. Consistency for GAN architectures suffered from inadequate constraints and failed to learn multiple and multi-modal transformations, which is inevitable for many medical applications. The main drawback is that it focused on creating an intermediate and workable hybrid, which is not permissible for the medical applications which focus on minute details. Another drawback is the weak interrelation between the two learning phases and TL and ReL have introduced improved coordination among them. We have demonstrated the capability of the novel network framework on public datasets. We emphasized that our novel architecture produced an improved neural image transformation version for the image, which is more acceptable to the medical community. Experiments and results demonstrated the effectiveness of our framework with enhancement compared to the previous works.
