  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.
