## 3 Results
### Classification of Generative AI Models
#### 3.2.7 Hybrid Models  
Hybrid generative AI models are models that combine multiple generative AI techniques or architectures to leverage their respective strengths and produce improved results. These models aim to overcome limitations or enhance the capabilities of individual generative models by integrating different approaches. _Adversarial autoencoder (AAE)_: AAE is a type of generative model that combines elements of both autoencoders and generative adversarial networks (GANs). It is designed to learn a compact latent representation of input data while generating realistic samples from that latent space. The autoencoder is integrated with a GAN framework in an adversarial autoencoder. The autoencoder acts as a generator network, taking in random noise and creating samples in the latent space. Instead of attempting to discriminate between actual and false samples, the discriminator network seeks to distinguish between samples from the true latent distribution and samples produced by the autoencoder in Figure 11.  
An AAE's training consists of two major stages, the reconstruction stage where the autoencoder is trained to correctly reconstruct the input data. It reduces the reconstruction loss between the input and output, which encourages the autoencoder to develop a meaningful representation. Coming to the second stage which is the adversarial stage, where the discriminator is trained to differentiate samples derived from the actual latent distribution from samples produced by the autoencoder. The generator (autoencoder) seeks to produce samples that deceive the discriminator. This adversarial training pushes the autoencoder to generate realistic latent space samples. The AAE may learn a compact latent representation that captures the main features of the input data while generating realistic samples from that latent space by combining the reconstruction and adversarial phases. Adversarial training prevents mode collapse and makes the generator explore its entire latent space. Adversarial autoencoders have been employed in a wide range of applications, including image generation [122], anomaly detection, and data synthesis. _PixelCNN:_ PixelCNN is a type of generative model that belongs to the family of autoregressive models and is specifically tailored for generating images pixel by pixel. It utilizes convolutional layers to capture spatial dependencies within the image. PixelCNN models the conditional probability distribution of each pixel given its preceding context. By modeling this conditional distribution, PixelCNN can generate images that exhibit realistic textures and local coherence. During training, PixelCNN is typically trained using maximum-likelihood estimation. The model takes an image as input and is trained to maximize the likelihood of generating that image. PixelCNN employs a process called autoregression for generating new images. It starts with an empty canvas and generates the pixels one by one, conditioning each prediction on the previously generated pixels. This autoregressive process allows the model to capture complex dependencies and generate coherent images. PixelCNN has  
Figure 11: Adversarial autoencoder architecture (AAE). demonstrated success in tasks such as image completion, super-resolution, and image synthesis [123]. _Variational Autoencoder with Generative Adversarial Networks (VAE-GAN):_ This hybrid model combines the generative capabilities of variational autoencoders (VAEs) and generative adversarial networks (GANs). The VAE component helps encode and decode input data, while the GAN component enhances the realism and diversity of the generated samples. Introspective adversarial networks [124] and Mol-CycleGAN [125] are examples of this combination. In introspective adversarial networks, there are other techniques to improve its performance, such as multiscale dilated convolution blocks and orthogonal regularization.