AI RESEARCH
KL Divergence Between Gaussians: A Step-by-Step Derivation for the Variational Autoencoder Objective
arXiv CS.LG
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ArXi:2604.11744v1 Announce Type: new Kullback-Leibler (KL) divergence is a fundamental concept in information theory that quantifies the discrepancy between two probability distributions. In the context of Variational Autoencoders (VAEs), it serves as a central regularization term, imposing structure on the latent space and thereby enabling the model to exhibit generative capabilities. In this work, we present a detailed derivation of the closed-form expression for the KL divergence between Gaussian distributions, a case of particular importance in practical VAE implementations.