Deep generative models have achieved impressive success in recent years. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), as powerful frameworks for deep generative model learning, have largely been considered as two distinct paradigms and received extensive independent study respectively. This paper establishes formal connections between deep generative modeling approaches through a new formulation of GANs and VAEs. We show that GANs and VAEs are essentially minimizing KL divergences with opposite directions and reversed latent/visible treatments, extending the two learning phases of classic wake-sleep algorithm, respectively. The unified view provides a powerful tool to analyze a diverse set of existing model variants, and enables to exchange ideas across research lines in a principled way. For example, we transfer the importance weighting method in VAE literatures for improved GAN learning, and enhance VAEs with an adversarial mechanism. Quantitative experiments show generality and effectiveness of the imported extensions.

本文介绍了针对生成模型学习中GAN和VAEs的新方法，并阐述了GAN和VAEs的相似性和差异性，探讨了它们的后验推理和KL散度的最小化问题，并将VAEs的importance weighting方法应用到GAN的学习中，同时采用对抗性机制加强VAEs模型，实验证明了这些方法的普适性和有效性。

深层生成模型的统一化