Abstract
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 realworld
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.