Image-to-Image Translation Using Generative Adversarial Network

Now a days, Generative Adversarial Networks (GANs) are an arising technology for both supervised and unsupervised learning which have capability to generate data of high standard. Image to Image translation is one of the application of GANs as a data augmentation which we have used in this proposed framework. Generative Networks makes the mapping between source image and target image easier and it calculates the loss function also to improve the quality of generated target image. In this paper, Conditional GANs are used which translates the images based upon some conditions. The performance is also analyzed of the model by doing hyper-parameter tuning.