## 3 Results
### Classification of Generative AI Models
#### 3.2.3 Generative Adversarial Networks (GAN)
By incorporating conditional variables, such as category labels, textual descriptions, or specific generated targets, CGANs provide guidance to the data generation process. This allows for supervised learning, targeted generation, and the ability to generate images with specific categories or labels. Moreover, CGANs can utilize image features as condition to generate corresponding word vectors, enabling effective cross-modal generation illustrated in Figure 7.  
Some of the GANs that incorporate this technique are conditional generative adversarial networks (CGAN) [81], CGAN with Pix2 Pix framework [82], conditional tabular GAN (CTGAN) [83], conditional generative adversarial networks with text (TAC-GAN, TAGAN) [67; 84]. _Wasserstein generative adversarial networks (WGANs)_ offer a novel approach to address the challenges faced by traditional GANs. By introducing the Wasserstein distance as a metric, WGANs provide a more stable training process and better gradient flow. The discriminator in WGANs, known as the \"critic\", assigns scores representing the distance between the real and fake data distributions [20]. This distance is measured using the Wasserstein distance instead of the Jensen-Shannon divergence or Kullback-Leibler divergence used in other generative models. WGANs mitigate the issue of mode collapse, where GANs fail to capture the full diversity of the data, by effectively learning the underlying data distribution, even for complex and high-dimensional datasets [85]. The generator and  
Figure 6: Typical structure of generative adversarial networks (GAN). discriminator in WGANs are trained to minimize the Wasserstein distance, encouraging the generator to generate samples that closely resemble real data. This enables WGANs to produce more diverse and realistic outputs. WGANs have found applications in various domains, such as image synthesis, text generation, and data augmentation. Their effectiveness in addressing mode collapse and providing a more reliable training process has made them a popular choice for researchers and practitioners working with generative models. _Deep convolutional generative adversarial networks (DCGANs)_ are a variant of GANs that leverage deep convolutional neural networks (CNNs) to enhance the quality of generated samples, particularly in the domain of image synthesis. DCGANs have proven to be highly effective in generating realistic and high-resolution images [86]. DCGANs utilize convolutional layers in both the generator and discriminator networks, allowing them to capture spatial dependencies and patterns in the data. DCGANs introduce several key design principles, including the use of convolutional and transposed convolutional layers, batch normalization, and ReLU activation functions. These principles contribute to the stability of the training process, mitigate issues like mode collapse and allow for the generation of diverse and high-quality samples. The benefits of DCGANs extend beyond image synthesis, with applications in areas such as image-to-image translation, style transfer, and data augmentation. The combination of deep convolutional architectures and adversarial training has propelled DCGANs as a go-to choice for generating visually appealing and realistic images in the field of deep learning. Generative adversarial networks (GANs) have revolutionized various domains of computer vision and machine learning. They can be classified into different categories based on their specific tasks and applications. Image-to-image translation GANs focus on translating images between domains, with subcategories such as CycleGAN [87], DiscoGAN [88], and DTN [89]. Super-resolution GANs [90] enhance the resolution of low-resolution images, including SRGAN and VSRResNetGAN [91]. Text-to-image GANs generate images from textual descriptions, exemplified by AttnGAN [92] and StackGAN [93]. Tabular data GANs generate synthetic tabular data, with examples like CTGAN [83] and TGAN [94].