Towards Model-Agnostic Posterior Approximation for Fast and Accurate Variational Autoencodersstat.ML
A new method approximates posterior for VAEs without iterative training.
problem Inference models in VAEs are poor early on, leading to suboptimal models.
method Train generative and inference models independently, using a model-agnostic posterior approximation (MAPA).
result MAPA approximates the true posterior deterministically and can improve density estimation.