Empirical Variational Autoencoder

Empirical Variational Autoencoder (EVA) replaces standard-Gaussian constraints with autoregressive latent priors learned via a single additional linear layer. By aligning the latent distribution gap between prior and posterior, the framework enables high-fidelity ancestral sampling for continuous-valued sequences in image and sound synthesis tasks.