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Amortized inference allows latent-variable models trained via variationallearning to scale to large datasets. The quality of approximate inference isdetermined by two factors: a) the capacity of the variational distribution tomatch the true posterior and b) the ability of the recognition network toproduce good variational parameters for each datapoint. We examine approximateinference in variational autoencoders in terms of these factors. We find thatdivergence from the true posterior is often due to imperfect recognitionnetworks, rather than the limited complexity of the approximating distribution.We show that this is due partly to the generator learning to accommodate thechoice of approximation. Furthermore, we show that the parameters used toincrease the expressiveness of the approximation play a role in generalizinginference rather than simply improving the complexity of the approximation.

Inference Suboptimality in Variational Autoencoders