## 2 What Is a Generative Adversarial Network?
### Advantages and Constrains of Generative Adversarial Networks
#### 2.5.2 Constraints
Therefore, the criteria provided in this paper provide a suitable framework for evaluating and selecting appropriate metrics for the evaluation of generative models, taking into account quality, stability, global capturability, robustness, efficiency, interpretability, and relevance to the specific task [57]. Current metrics mainly focus on the comparison of feature distributions between real and GAN-generated data. In these cases, the visual quality is expected to be an indirect result of the evaluation and is not optimized or tested directly using these metrics. It is important to note that, despite the effectiveness of existing metrics in quantitatively assessing the statistical similarity between real and generated data, the assessment of visual quality remains an open challenge in the field of GANs. So far, no metric has been developed that can directly assess the perceptual quality of the generated images in an effective way [86]. Finally, it can be said that an effective evaluation metric for generative models, such as GANs, must meet several key criteria. It must be able to distinguish between models that generate high-quality samples and those that do not, as well as be robust to small perturbations in the input data. In addition, the measure must capture the overall structure of the generated samples, be sensitive to mode dropping to detect deficiencies in the generation of specific patterns and be computationally efficient for practical application. Interpretability is essential for understanding the results, and the choice of metric must be aligned with the specific task it seeks to address, ensuring its relevance in the context of the application [83; 84]. From a reasonable view, it is crucial to mention that GANs face several challenges, including instability between the generator and discriminator. It is essential to balance the strictness of the discriminator to ensure that it is not too strict. Another challenge is determining the correct positioning of objects in an image, which can result in the generator creating unrealistic images. GANs also struggle to understand the global or holistic structure of images, similar to the perspective problem. In artificial intelligence and machine learning, GANs stand as a potent tool. Their versatility allows for their utilization across various tasks, showcasing distinct advantages over alternative generative models, including autoencoders, variational autoencoders, Boltzmann machines, PixelCNN, and generative flow models. A series of negative effects that GAN networks can have are listed, such as the generation of faces used in false profiles on social networks to commit criminal acts and the generation of false images and videos of real people without their permission. When GANs generate synthetic faces, they commonly focus solely on the facial region, often neglecting the rest of the body. In some cases, specific models may only generate a mask of the face. Although these frontal views of the faces can be caught and utilized as synthetic image data, there are several challenges associated with GAN training. As a result, the generated images may exhibit various flaws and imperfections. Moreover, GANs typically require much training data to achieve satisfactory results. Limited availability or small dataset sizes can pose challenges and hinder the quality of generated outputs. Moreover, GANs are vulnerable to mode collapse, in which the generator fails to capture the desired diversity in the data distribution and produces a limited range of outputs. This can lead to a need for more variety in the generated images. Addressing these challenges is an active area of research, and advancements are being made to improve GAN-generated image stability, diversity, and overall quality [87].