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Image to image transformation has gained popularity from different researchcommunities due to its enormous impact on different applications, includingmedical. In this work, we have introduced a generalized scheme for consistencyfor GAN architectures with two new concepts of Transformation Learning (TL) andRelative Learning (ReL) for enhanced learning image transformations.Consistency for GAN architectures suffered from inadequate constraints andfailed to learn multiple and multi-modal transformations, which is inevitablefor many medical applications. The main drawback is that it focused on creatingan intermediate and workable hybrid, which is not permissible for the medicalapplications which focus on minute details. Another drawback is the weakinterrelation between the two learning phases and TL and ReL have introducedimproved coordination among them. We have demonstrated the capability of thenovel network framework on public datasets. We emphasized that our novelarchitecture produced an improved neural image transformation version for theimage, which is more acceptable to the medical community. Experiments andresults demonstrated the effectiveness of our framework with enhancementcompared to the previous works.

ReLGAN: Generalization of Consistency for GAN with Disjoint Constraints  and Relative Learning of Generative Processes for Multiple Transformation  Learning