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.

在过去的几年里，深度学习领域发生了几次革命，主要是生成对抗网络 (GAN) 的巨大影响。GAN 不仅在定义模型时提供了独特的架构，而且还产生了令人难以置信的结果，这些结果对社会产生了直接影响。由于 GAN 带来的显着改进和新的研究领域，社区不断提出新的研究，几乎无法跟上时代的步伐。我们的调查旨在提供 GAN 的总体概述，展示最新的架构、损失函数的优化、验证指标和最广泛认可的变体的应用领域。将评估模型架构的不同变体的效率，以及展示最佳应用领域；作为该过程的重要组成部分，将分析评估 GAN 性能的不同指标和常用的损失函数。本次调查的最终目的是总结 GAN 的演进和性能，这些 GAN 正在取得更好的成果，以指导该领域未来的研究人员。