The variational autoencoder (VAE) is a popular model for density estimation and representation learning. Canonically, the variational principle suggests to prefer an expressive inference model so that the variational approximation is accurate. However, it is often overlooked that an overly-expressive inference model can be detrimental to the test set performance of both the amortized posterior approximator and, more importantly, the generative density estimator. In this paper, we leverage the fact that VAEs rely on amortized inference and propose techniques for amortized inference regularization (AIR) that control the smoothness of the inference model. We demonstrate that, by applying AIR, it is possible to improve VAE generalization on both inference and generative performance. Our paper challenges the belief that amortized inference is simply a mechanism for approximating maximum likelihood training and illustrates that regularization of the amortization family provides a new direction for understanding and improving generalization in VAEs.

本论文提出了一种控制推理模型平滑性的技术 - Amortized Inference Regularization(AIR)，该技术能够提高VAE模型的推理和生成性能，并挑战了对amortized inference仅仅只是最大似然训练的一种近似方法的信仰，为理解和提高VAEs中的泛化能力提供了新方向。

摊销推断正则化