We present an information-theoretic framework for understanding trade-offs in unsupervised learning of deep latent-variables models using variational inference. This framework emphasizes the need to consider latent-variable models along two dimensions: the ability to reconstruct inputs (distortion) and the communication cost (rate). We derive the optimal frontier of generative models in the two-dimensional rate-distortion plane, and show how the standard evidence lower bound objective is insufficient to select between points along this frontier. However, by performing targeted optimization to learn generative models with different rates, we are able to learn many models that can achieve similar generative performance but make vastly different trade-offs in terms of the usage of the latent variable. Through experiments on MNIST and Omniglot with a variety of architectures, we show how our framework sheds light on many recent proposed extensions to the variational autoencoder family.

本文讨论了深度非监督学习中最大化证据下限（ELBO）的训练方法，并推导了输入与潜变量之间互信息的下限和上限，进而通过率失真曲线描述了压缩和重构准确性之间的平衡，并提出了一个新的方法来避免强大的随机解码器忽略它们的潜变量。

修复破损的ELBO