Generative Adversarial Network

INTRODUCTION: In recent research, we have seen multiple CNN classification, detection and segmentation algorithms such as Inception versions, YOLO versions, FCN, U-net and KSAC. All high accuracy model requires a large amount of data along with labelling. In the real world, data are usually in the unlabeled form. So, data gathering and labelling are really time-consuming and erroneous. DCGAN can be helpful to learn intermediate features from the unlabeled data. The feature representation can be leveraged for further all types of supervised tasks. One might know that GAN contains two types of architecture: Discriminator Generator After training of GAN: Discriminators can be used for image classification tasks. Generators can be used to manipulate the semantic qualities of generated images. DETAIL OF DCGAN: DCGAN is basically used to stable GANs for convolution in most settings. It also helps to visualize filters that have been learnt to detect specific shapes. Using generator input vect...

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...