## 3 Results
### Classification of Generative AI Models
#### 3.2.7 Hybrid Models
_Transformer with Long Short-Term Memory (LSTM):_ The transformer architecture with long short-term memory (LSTM) is a type of recurrent neural network known for its ability to capture long-term dependencies in sequential data. Transformers are powerful models for sequence processing, leveraging self-attention mechanisms to capture dependencies across the sequence. The GTR-LSTM [129] encoder provides a graph-based approach to encoding triples, considering the structural relationships between entities in a knowledge graph. By incorporating attention mechanisms and entity masking, the model aims to generate coherent and meaningful output sequences based on the input graph. _Vision Transformers with Residual Neural Networks (ResNet):_ Vision transformers leverage the self-attention mechanism of transformers to capture long-range dependencies and enable effective modeling of image data. The combination of ResNet and vision transformers can benefit from both the local feature extraction capabilities of ResNet and the global context understanding of vision transformers, resulting in improved image understanding and representation [130]. _Diffusion probabilistic models with Contrastive Language-Image Pretraining (CLIP):_ Diffusion modeling is a powerful technique for modeling complex data distributions and generating high-quality samples. CLIP, on the other hand, is a state-of-the-art method for learning visual representations from images and corresponding textual descriptions. DiffusionCLIP [131] combines the power of diffusion modeling and the guidance of CLIP to enable precise and controlled image manipulation. It leverages pretrained diffusion models and the CLIP loss to fine-tune the diffusion model and generate samples that align with a target textual description, which opens new possibilities for image generation and manipulation tasks. _Convolutional Neural Network (CNN) with Bidirectional Encoder Representations from Transformers (BERT):_ CLAP (contrastive learning for audio and text pairing) [47] is a model that jointly trains an audio encoder and a text encoder to learn the similarity or dissimilarity between audio and text pairs. The goal is to enable zero-shot classification by computing embeddings for audio and text and using cosine similarity to measure their similarity. The model takes audio and text pairs as input, which are separately processed by the audio encoder and text encoder. The encoders extract meaningful representations from the audio and text inputs. These representations are then projected into a joint multimodal space using linear projections. _Convolutional Sequence-to-Sequence Learning (ConvS2S):_ This is a neural network architecture that was introduced for sequence-to-sequence tasks, such as machine translation or speech recognition. It leverages convolutional neural networks (CNNs) to process input sequences and generate output sequences, providing an alternative to the commonly used recurrent neural networks (RNNs) Unlike RNN-based models that rely on sequential processing, ConvS2S [132] applies parallel convolutions across the input sequence. This enables more efficient computation and allows for better utilization of parallel processing capabilities, leading to faster training and inference times. The use of convolutions also helps capture local dependencies in the input sequence, which can be beneficial for tasks where context is primarily determined by nearby elements. The architecture of ConvS2S typically consists of an encoder and a decoder. The encoder is composed of several layers of 1D convolutional filters followed by non-linear activation functions. These filters capture different patterns and features in the input sequence, allowing for effective representation learning. The decoder, on the other hand, employs similar convolutional layers but with additional techniques like attention mechanisms to generate the output sequence. Unlike RNN-based models that rely on sequential processing, ConvS2S applies parallel convolutions across the input sequence.