Hybrid Deep Convolutional Generative Adversarial Networks (DCGANS) and Style Generative Adversarial Network (STYLEGANS) Algorithms to Improve Image Quality

Abstract

Generative Adversarial Networks or GANS, are another way of achieving generative modeling using various deep learning methods like convoluted neural networks. They have a wide range of applications like :- image to image translation, improving the resolution of the images, creating multiple images using a single image, checking if the image is real or fake and the list goes on. One of the implementations of GANS, i.e, DCGANS would be discussed in this paper. DCGANS stands for Deep Convolutional Generative Adversarial Networks, it uses convolutional and convolutional-transpose layers in the generator and discriminator, respectively. This DCGANS give prompt result while processing many real time images. In their study Unsupervised representation Learning with Deep Convolutional Generative Adversarial Networks, Radford et al. proposed it. This algorithm generates fake images after being trained on a dataset of human faces. However to create these fake images, the quality of these images should be compromised and this can also make them easily identifiable as fake image. So, here StyleGANS will be used as another implementation of GANS. StyleGANS is not only used for improving the quality of these images but also it helps us to increase fakeness of the images. This project uses the CelebA dataset from Kaggle to train the models.