ED-VAE：变分自编码器中ELBO的熵分解
ED-VAE: Entropy Decomposition of ELBO in Variational Autoencoders
AI总结：
针对传统VAE受ELBO公式限制、难以适配复杂先验且生成与可解释性不足的问题，提出ED-VAE，通过显式引入熵和交叉熵重构ELBO，提升模型灵活性、潜在空间可解释性与生成性能。
摘要：
传统变分自编码器（VAE）受证据下界（ELBO）公式的局限性约束，尤其是在使用简单、非解析或未知先验分布时。这些局限性制约了VAE生成高质量样本、提供清晰可解释潜在表示的能力。本研究提出熵分解变分自编码器（ED-VAE），这是一种对ELBO的全新重构形式，显式包含熵与交叉熵分量。该重构大幅提升了模型灵活性，支持整合复杂与非标准先验。通过对潜在空间的编码和正则化提供更精细的控制，ED-VAE不仅提升了可解释性，还能有效捕捉潜在变量与观测数据间的复杂交互，从而带来更优的生成性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
