Nowadays high technology ability changes reality into “realities” making almost impossible for human eye to recognize and known exactly who is who and what is what.
Unsupervised image-to-image translation aims at learning a joint distribution of images in different domains by using images from the marginal distributions in individual domains. Since there exists an infinite set of joint distributions that can arrive the given marginal distributions, one could infer nothing about the joint distribution from the marginal distributions without additional assumptions. To address the problem, we make a shared-latent space assumption and propose an unsupervised image-to-image translation framework based on Coupled GANs. We compare the proposed framework with competing approaches and present high quality image translation results on various challenging unsupervised image translation tasks, including street scene image translation, animal image translation, and face image translation. We also apply the proposed framework to domain adaptation and achieve state-of-the-art performance on benchmark datasets.
In a world where information is ,as never in past, manipulated and challenged continuously by fake news and fake military reports to engage military strikes against undesired enemies, we have just crossed a dangerous line that could essentially lead all of us in a no return point and a dead zone.
Thus 1984 State enters in full operational status, a proper reality matrix with unpredictable results.