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Variational Autoencoders (VAEs) have become a cornerstone in generativemodeling and representation learning within machine learning. This paperexplores a nuanced aspect of VAEs, focusing on interpreting the KullbackLeibler (KL) Divergence, a critical component within the Evidence Lower Bound(ELBO) that governs the trade-off between reconstruction accuracy andregularization. While the KL Divergence enforces alignment between latentvariable distributions and a prior imposing a structure on the overall latentspace but leaves individual variable distributions unconstrained. The proposedmethod redefines the ELBO with a mixture of Gaussians for the posteriorprobability, introduces a regularization term to prevent variance collapse, andemploys a PatchGAN discriminator to enhance texture realism. Implementationdetails involve ResNetV2 architectures for both the Encoder and Decoder. Theexperiments demonstrate the ability to generate realistic faces, offering apromising solution for enhancing VAE based generative models.

GAMIX-VAE: A VAE with Gaussian Mixture Based Posterior