  In the last few years, there have been several revolutions in the field of
deep learning, mainly headlined by the large impact of Generative Adversarial
Networks (GANs). GANs not only provide an unique architecture when defining
their models, but also generate incredible results which have had a direct
impact on society. Due to the significant improvements and new areas of
research that GANs have brought, the community is constantly coming up with new
researches that make it almost impossible to keep up with the times. Our survey
aims to provide a general overview of GANs, showing the latest architectures,
optimizations of the loss functions, validation metrics and application areas
of the most widely recognized variants. The efficiency of the different
variants of the model architecture will be evaluated, as well as showing the
best application area; as a vital part of the process, the different metrics
for evaluating the performance of GANs and the frequently used loss functions
will be analyzed. The final objective of this survey is to provide a summary of
the evolution and performance of the GANs which are having better results to
guide future researchers in the field.
