Gaussian Latent

Gaussian latent variable models are a cornerstone of generative modeling, aiming to learn efficient low-dimensional representations of high-dimensional data by assuming a Gaussian distribution in the latent space. Current research focuses on improving these models by addressing limitations of the Gaussian assumption, such as through the use of alternative distributions (e.g., Epanechnikov kernels, Gaussian mixtures) within variational autoencoders (VAEs) or by developing novel methods for manipulating and combining latent variables in diffusion models. These advancements enhance the quality, interpretability, and efficiency of generative models, with significant implications for applications like data augmentation, synthetic data generation, and uncertainty estimation in various fields.