Variational Autoencoders (VAEs) have become a cornerstone in generative modeling and representation learning within machine learning. This paper explores a nuanced aspect of VAEs, focusing on interpreting the Kullback Leibler (KL) Divergence, a critical component within the Evidence Lower Bound (ELBO) that governs the trade-off between reconstruction accuracy and regularization. While the KL Divergence enforces alignment between latent variable distributions and a prior imposing a structure on the overall latent space but leaves individual variable distributions unconstrained. The proposed method redefines the ELBO with a mixture of Gaussians for the posterior probability, introduces a regularization term to prevent variance collapse, and employs a PatchGAN discriminator to enhance texture realism. Implementation details involve ResNetV2 architectures for both the Encoder and Decoder. The experiments demonstrate the ability to generate realistic faces, offering a promising solution for enhancing VAE based generative models.

该论文探讨了变分自编码器（VAEs）的一个微妙方面，着重解释了Kullback Leibler（KL）散度，这是Evidence Lower Bound（ELBO）中的一个关键组成部分，用于平衡重构准确性和正则化之间的权衡。通过使用混合高斯后验概率重新定义ELBO，引入正则化项以防止方差崩溃，并使用PatchGAN鉴别器增强纹理真实性，该方法在实现细节中采用了ResNetV2架构用于编码器和解码器。实验证明其能够生成逼真的人脸，为增强基于VAE的生成模型提供了有希望的解决方案。

GAMIX-VAE: 基于高斯混合后验的VAE