EVIA: Entropic Variational Inference Auto-encoding

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.