Generative adversarial networks: A comprehensive review and the way forward

The deep learning ability to recognize patterns in data has recently become popular within education. Created in 2014, generative adversarial networks (GANs) are innovative classes of deep learning generative models based on game theory and consist of two players. GANs generate data from scratch using two neural networks: the generator and the discriminator. Since their creation, GANs have been utilized in many applications and have advantages and disadvantages. In light of such a long journey, evaluating the technology is essential as it provides readers with the way forward. To this end, this paper reviews GANs and explores some fundamental challenges that develop during evaluation and training. We also discuss GANs' challenges and elaborate subsequent solutions. Through a single context, we explain the reasoning behind the GAN technology and examine its direction and motivation. We discuss different variants of GANs and real-world application examples, including performance evaluation metrics across various sectors. We consider results obtained recently and highlight ideas for further investigation. This detailed retrospect will give the reader a better understanding of the possible uses of GANs. It will also show how they can help address current issues in a variety of disciplines. Before that, the paper reviews GANs' architectures and network approaches and elaborates on challenges and solutions. The reader is then guided through the literature on the various applications of GANs and the importance of the research interest associated with GANs. As a final step, we suggest the way forward and conclude the review.