Traditional Variational Autoencoders (VAEs) are constrained by the limitations of the Evidence Lower Bound (ELBO) formulation, particularly when utilizing simplistic, non-analytic, or unknown prior distributions. These limitations inhibit the VAE's ability to generate high-quality samples and provide clear, interpretable latent representations. This work introduces the Entropy Decomposed Variational Autoencoder (ED-VAE), a novel re-formulation of the ELBO that explicitly includes entropy and cross-entropy components. This reformulation significantly enhances model flexibility, allowing for the integration of complex and non-standard priors. By providing more detailed control over the encoding and regularization of latent spaces, ED-VAE not only improves interpretability but also effectively captures the complex interactions between latent variables and observed data, thus leading to better generative performance.

这项工作介绍了熵分解变分自编码器（ED-VAE），它是对ELBO的新的重新制定，明确包括熵和交叉熵组件。通过提供对潜空间的编码和正则化的更详细控制，ED-VAE不仅提高了可解释性，还有效捕捉到潜在变量和观测数据之间的复杂相互作用，从而提高了生成性能。

ED-VAE：变分自编码器中ELBO的熵分解