## 2 What Is a Generative Adversarial Network?
### Main Metrics Used  
To conduct this literature review, the authors in [9] introduced and applied the FID metric, highlighting its relevance in evaluating the results generated by GANs. The main focus was on understanding how the FID metric has been employed in various investigations. However, during the course of this review, it was observed that many studies simply use the FID metric without addressing essential technical aspects, such as data preparation or balance in the dataset. This observation led us to broaden our search beyond papers that only use the FID metric. We included research proposing new metrics aimed at assessing the quality of results generated by GANs, which allowed for a more complete and comprehensive view of evaluation in this field. The correct use of metrics in GANs is of the utmost importance for accurately assessing the performance of these models. Choosing appropriate metrics for evaluation can make or break the results of a scientific paper, as it can affect the validity and reliability of the findings. Some of the most commonly used metrics with a brief description are as follows:  
* _Inception Score (IS)_: This measures the quality and diversity of generated images by using a pre-trained Inception-v3 classifier network to calculate the average probability distribution of classes for the generated images. The score is based on two factors: the quality of the generated images (measured using the entropy of the class distribution)  
Figure 4: The frequency of main datasets in the literature review. and the diversity of the generated images (measured using the Kullback-Leibler divergence between the class distribution of the generated images and that of the training set). A higher IS indicates that the generated images are high quality and diverse [56]. * _Frechet Inception Distance(FID)_: The similarity between the feature distributions of real and generated images are quantified using a high-dimensional feature space. It uses a pre-trained Inception-v3 network to extract features from both sets of images and calculates the FID between their distributions. A lower value indicates that the generated images are more similar to the real images regarding their high-level features [47; 57]. * _Perceptual Image Patch Similarity (LPIPS)_: This is a metric that quantifies the perceptual similarity between image pairs by comparing their feature representations in a deep neural network. It calculates the distance between the feature representations of the two images at multiple layers of a pre-trained network and aggregates these distances to derive a similarity score. A lower score indicates a higher perceptual similarity between the generated and real images [58]. * _Precision and Recall_: These are standard measures used in machine learning to evaluate the performance of classifiers. They can also be used to evaluate the performance of GANs by comparing the generated images to real images and computing the precision and recall of the generated images for the real images [59]. An important comment concerning precision and recall is that they may not be the most appropriate metrics for evaluating the performance of GANs, as they only provide information about the ability of the models to identify positive samplings correctly and may not capture other aspects of the image quality, such as diversity, realism, and perceptual similarity. * _Structural Similarity Index (SSIM)_: This measures the similarity between pairs of images based on their luminance, contrast, and structural similarities. It compares the pixel values of the two images at each point in a local window, taking into account the contrast and structural similarities between the regions of the window. A higher value indicates that the developed images are more comparable to the real images regarding their structural properties [60]. * _Mean Squared Error (MSE)_: This measures the average squared difference between the pixel values of the generated and real images.