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.

翻译：在过去的几年中，深度学习领域经历了数次革命，其中生成对抗网络（GANs）的巨大影响尤为突出。GANs不仅为模型定义提供了独特的架构，还生成了对社会产生直接影响的可观成果。由于GANs带来了显著的进步和新研究领域，学术界不断涌现新的研究成果，使得人们几乎难以跟上时代步伐。本综述旨在概述GANs，展示最新架构、损失函数优化、验证指标以及最广泛认可变体的应用领域。我们将评估不同模型架构变体的效率，并指出其最佳应用领域；作为过程的关键部分，还将分析评估GANs性能的常用指标和损失函数。本综述的最终目标是总结表现更优的GANs的演进与性能，以指导该领域的未来研究者。