EVIA: Entropic Variational Inference Auto-encoding

摘要

We present Entropic Variational Inference Auto-encoding (EVIA), a novel framework that extends classical variational approaches—including WAEs and AAEs, by incorporating entropy regularization. This regularization yields an entropic objective characterized by a closed-form Gibbs posterior, established via the Donsker–Varadhan representation. Consequently, this formulation facilitates efficient sampling strategy, allowing the model to perform high-fidelity autoencoding while jointly ensuring accurate distributional alignment. Empirical results demonstrate EVIA’s strong, consistent performance across diverse generative modeling tasks, including posterior estimation, variational inference, and image inpainting.