Generative Adversarial Network

  INTRODUCTION Image to image conversion is a really hard task to do when you don't have any specific output label image. There are also other approaches as follows: Unpair image to image translation  can translate one image to another but the model can be trained on a specific translation. Neural style transfer translates input images on a specific painting or style. but it can not convert from a style to back to the original. Using cyclic GAN , an image can convert to any other style passed in the model at that time as a reference style image. and the model also helps to convert back from a style to original by just changing the location of passing images to the model. WORK OF CYCLIC-GAN The main goal of the cyclic model is to map image A to image B and vice versa. The cyclic model contains two GAN models one map image A to image B and another map B image to image A. G : X ↦ Y ⇒ G ( X ) F : Y ↦ X ⇒ F ( G ( X ) ) ≈ X Where,      G is a GAN model that maps image...

Introduction: In recent times, lots of research have been going on, and many new techniques have been introduced in the Deep Learning domain. Especially, the development has been made in the computer vision area such as Deepface and  3D model generation  using 2d images and many more. There is also a subdomain of vision area which can generate unseen images by training a Generative adversarial network(GAN). There are multiple ways to generate images depending on applications. But, there are two models which can be used mostly. DCGAN (Deep convolution GAN) cyclicGAN Explanation: One can check the following criteria to distinguish between these two models. Architecture: DCGAN has basic GAN architecture which contains a generator and discriminator. The input of the generator is a random numbers array from any distribution (normal/uniform). Figure 1:   DCGAN functional diagram CyclicGAN has a complex architecture that contains two generators and discriminators. One GAN archi...