A Review on Generative Adversarial Networks
Dilum Maduranga De Silva, Guhanathan Poravi · 2021
Generative Adversarial Networks are one of the widely discussed topics in the domain of deep learning. This review paper discusses about the differences in the existing generative adversarial network architectures in terms of their strengths and limitations. Moreover, paper starts the discussion with a comparison of generative models and discriminative models. We will also give a brief introduction to the general GAN architecture including various applications of GANs and finally will elaborate on future work. Furthermore, this paper is an initial phase of an ongoing research and in future, authors of this paper hope to make use of this knowledge to address the identified gaps and future work in the existing literature.