## 3 Results
### Classification of Generative AI Models
#### 3.2.7 Hybrid Models
These techniques help the model to capture long-range dependencies in the image, prevent overfitting, and generate images that are more realistic and coherent. Mol-CycleGAN extends the CycleGAN framework to molecular embeddings in the latent space of JT-VAE utilizes the latent space of JT-VAE (junction tree variational autoencoder) as the embedding representation. The latent space is created by a neural network during the training process. The advantage of using the latent space embedding is that the distance between molecules can be defined directly in this space, enabling the calculation of the loss function. VAE-GANs have been successfully applied in various domains, including image synthesis, text generation, and music composition. _Generative Adversarial Networks (GAN) with Dense Convolutional Neural Networks (DenseNet) or Residual Neural Networks (ResNet):_ Dense convolutional neural networks (DenseNet) are known for their dense connections, which facilitate feature reuse and enhance the flow of gradients throughout the network. DenseNet architectures have shown remarkable performance in image classification tasks by capturing intricate patterns and representations in the data. When combined with generative adversarial networks (GANs), DenseNet can contribute to the generator component of the GAN framework. By utilizing DenseNet as the generator, the hybrid model benefits from its powerful feature learning capabilities and the ability to capture complex patterns and details in the data. ResNet is also used in similar way but there is a slight difference between them ResNet's skip connections enable training of very deep networks, while DenseNet's dense connectivity promotes parameter efficiency and better information flow. This combination of models is done in CycleGAN [54], PGGAN [126]. CycleGAN is a powerful framework for unsupervised image translation by leveraging the concept of cycle consistency and utilizing architectures such as ResNet and PatchGAN to achieve impressive results in various image-to-image translation tasks. The PGGAN discriminator, formed by combining PatchGAN and G-GAN, provides fine-grained evaluation of local image patches and incorporates gradient penalty regularization, enhancing the training stability and diversity of generated samples in the PGGAN framework. _Generative Adversarial Networks (GAN) with Recurrent Neural Networks (RNN) or Convolutional Neural Networks (CNN):_ Combining RNNs or CNNs with GANs, it becomes possible to generate sequences that possess both coherence and realism. The RNN component provides the ability to model sequential dependencies, ensuring that the generated sequences flow naturally and exhibit contextual understanding. The GAN component, on the other hand, improves the diversity and quality of the generated sequences by leveraging the adversarial training process. In the RTT-GAN [62], the generator of the GAN employs a hierarchical structure and attention mechanisms to retain contextual states at various levels and a hierarchical structure and attention mechanisms to retain contextual states at various levels. The hierarchy is formed by a paragraph-level recurrent neural network (RNN), a sentence-level RNN, and a word-level RNN, along with two attention modules. The paragraph RNN encodes the current paragraph state by considering preceding sentences. The spatial-visual attention module selectively focuses on semantic regions, guided by the current paragraph state, to generate the visual representation of the sentence. Consequently, the sentence RNN can encode a topic vector for the newly generated sentence. The discriminator LSTM RNN takes the sentence embeddings of all preceding sentences as inputs. It computes the topic smoothness value of the current constructed paragraph description at each recurrent step, assessing the coherence of topics across the generated sentences. With these multi-level assessments, the model can generate long yet realistic descriptions, maintaining both sentence-level plausibility and topic coherence.