The difficulties in matching the latent posterior to the prior, balancing powerful posteriors with computational efficiency, and the reduced flexibility of data likelihoods are the biggest challenges in the advancement of Variational Autoencoders. We show that these issues arise due to struggles in marginal divergence minimization, and explore an alternative to using conditional distributions that is inspired by Generative Adversarial Networks. The class probability estimation that GANs offer for marginal divergence minimization uncovers a family of VAE-GAN hybrids, which offer the promise of addressing these major challenges in variational inference. We systematically explore the solutions available for distribution matching, but show that these hybrid methods do not fulfill this promise, and the trade-off between generation and inference that they give rise to remains an ongoing research topic.

本文揭示了Variational Autoencoders (VAEs)的局限性，它们无法稳定地学习潜在空间和可见空间中的边缘分布，因为学习过程是通过匹配条件分布进行的。同时，文章通过大规模的评估VAE-GAN混合模型并分析类概率估计对分布学习的影响，得出VAE-GAN混合模型在可扩展性、评估和推理方面比VAE更加困难，无法提高GAN的生成质量。

变分推断中的分布匹配