Latent Space Representation Learning Based on Variational Autoencoders

Abstract

World models constitute an important research direction in artificial intelligence, aiming to enable machines to perceive, understand, and predict the external environment in a manner similar to humans. Representation learning in latent space, serving as the foundation of the perception layer in world models, determines the model's capacity for compressing and understanding environmental states. The Variational Autoencoder (VAE) is a deep generative model that achieves effective mapping from high-dimensional observational data to low-dimensional latent states through variational inference. This paper first introduces the fundamental concepts of latent space and the working principles of autoencoders, and then elaborates on the evolution from traditional autoencoders to variational autoencoders. Subsequently, it analyzes the core mathematical framework of the VAE, including variational inference, the evidence lower bound, and the reparameterization trick. Finally, it discusses the application value and existing limitations of this model in the perception layer of world models. The study demonstrates that the VAE possesses significant advantages in state compression and uncertainty modeling, but suffers from issues such as blurry image generation and oversimplified posterior distribution assumptions. Future research may focus on the integration of diffusion models with variational autoencoders and causal representation learning.