Variational Autoencoder Frameworks in Generative AI Model

The research provides a comprehensive review of generative architectures built upon the Variational Autoencoder (VAE) paradigm, emphasizing their capacity to delineate latent structures inherent to input datasets. Beginning with a foundational understanding of autoencoders, the paper progresses to discuss advancements and augmentations in the domain. The Variational Autoencoder introduces a pivotal element called the Evidence Lower Bound, facilitating the extraction of latent variables from an underlying probability distribution. An enhancement to the VAE is the ß-Variational Autoencoder, which exhibits superior proficiency in the disentanglement of these latent attributes. Building on this foundation, the ß-Total Correlation Autoencoder further optimizes this capability, demonstrating marked improvements over the ß-Variational Autoencoder in the discernment of latent feature spaces.