## 3 Results
### Classification of Generative AI Models
#### 3.2.7 Hybrid Models
In the CNN-GAN model, a convolutional encoder-decoder network is utilized for generating new content by jointly training it with adversarial networks. This training setup aims to ensure coherence between the generated pixels and the existing ones. These CNN-based methods have demonstrated the ability to generate realistic and plausible content in highly structured images, such as faces, objects, and scenes [127]. _Generative Adversarial Networks (GAN) with Denoising Diffusion Probabilistic Models (DDPM) and Transformers:_ Combination DDPMs, GANs, and transformers can create a hybrid generative AI model with enhanced capabilities. This combination allows for the generation of diverse and high-quality samples while leveraging the strengths of each component. DiffGAN-TTS [128] and ProDiff [53] implement this combination of models. DiffGAN-TTS is a novel text-to-speech (TTS) model that achieves high-fidelity and efficient speech synthesis. It takes inspiration from the denoising diffusion GAN model and models the denoising distribution using an expressive acoustic generator. This generator is trained adversarially to match the true denoising distribution, ensuring high-quality output spectrograms. DiffGAN-TTS ability to allow large denoising steps during inference. This reduces the number of denoising steps required and accelerates the sampling process. To further enhance sampling efficiency, DiffGAN-TTS incorporates an active shallow diffusion mechanism. ProDiff utilizes generator-based parameterization, where the denoising model directly predicts clean data using a neural network [53]. This approach has shown advantages in accelerating sampling from complex distributions. By directly predicting clean data, ProDiff avoids the need to estimate gradients and achieves faster synthesis. _Transformer with Recurrent Neural Network (RNN):_ The combination of transformers and RNNs can leverage the strengths of both architectures, allowing for improved modeling of sequential data with long-term dependencies and global context understanding. This combination is useful for tasks such as speech recognition, time series forecasting, and video processing, where both local temporal dependencies and global context are crucial for accurate predictions. MolT5 [58] implements three baseline models for the tasks of molecule captioning and molecule generation. The first baseline is a four-layer GRU recurrent neural network with a bidirectional encoder. This model leverages the sequential nature of the data and captures contextual information from both past and future. The second baseline is based on the transformer architecture, consisting of six encoder and decoder layers. Transformers utilize self-attention mechanisms to capture global dependencies and have been successful in various sequence-to-sequence tasks. The third baseline is based on the T5 model, a pre-trained sequence-to-sequence model. Three T5 checkpoints, namely small, base, and large, are fine-tuned for molecule captioning and molecule generation. T5 models have shown strong performance in natural language processing tasks. _Transformer with Graph Convolutional Network (GCN):_ For tasks that require graph-structured data, this hybrid model combines the strength of transformers and GCNs. Transformers excel at sequence-to-sequence tasks and have demonstrated success in natural language processing and image processing. GCNs, on the other hand, are especially intended to handle graph-structured data and capture node relationships. This hybrid model can effectively capture both the sequential dependencies of the data and the graph-based relationships by combining transformers and GCNs, enabling enhanced modeling and representation learning in graph-based tasks such as node classification, link prediction, graph generation and molecule structure generation [60].