## 5 Conclusions and Future Work
### Future Work  
Therefore, it is imperative to focus on improving the quality of synthetic images generated by StyleGAN to create images that are indistinguishable from real photos of people. In achieving this, we can enhance the accuracy and reliability of these models, making them more useful in various applications. Future challenges include:  
* Define a way to use the most representative metrics. This is important because different metrics may have different strengths and weaknesses. It is crucial to select the appropriate metrics that can effectively capture the quality and diversity of the generated images. In addition, combining multiple metrics, such as FID, IS, and LPIPS, can provide a more comprehensive evaluation of the GAN models.
* Implement a new way to support the existing metrics with a VAE. This approach could improve the quality of synthetic images by incorporating additional information from a VAE, which can learn a better latent representation of the images and improve the diversity of the developed images.
* Define a standard dataset or the minimum features of a dataset like proper segmentation, resolution, etc. This can establish a benchmark for evaluating GAN models and enable more meaningful comparisons between different models. Having a standardized dataset with segmentation could also help to identify the strengths and weaknesses of different models in generating specific features of the images.
* Implement a framework that allows us to find the best way to evaluate synthetic images. This could involve exploring different combinations of metrics and comparing their performance on a standardized dataset. It could also include investigating the effectiveness of different preprocessing methods, such as data augmentation or normalization, in improving the quality of synthetic images.
* Defining a standard of minimum features and guidelines for creating a balanced dataset could also reduce the computational power used by GANs, which requires large amounts of data to generate synthetic data. Using a standardized dataset with balanced features, GANs may require less computational power to produce accurate results, which could reduce the costs and resources required for training and using these models.  
**Author Contributions:** Conceptualization, C.N.V.M., A.L.S.O. and L.J.G.V.; methodology, C.N.V.M., A.L.S.O. and L.J.G.V.; validation, C.N.V.M., A.L.S.O. and L.J.G.V.; investigation, C.N.V.M., A.L.S.O. and L.J.G.V. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work also was supported by the European Commission under the Horizon 2020 research and innovation programme, as part of the project HEROES (Grant Agreement no. 101021801). Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or European Commission--EU. Neither the European Union nor the European Commission can be held responsible for them.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable. This study does not report any data.  
**Conflicts of Interest:** The authors declare no conflict of interest.