Probabilistic generative models can be used for compression, denoising, inpainting, texture synthesis, semi-supervised learning, unsupervised feature learning, and other tasks. Given this wide range of applications, it is not surprising that a lot of heterogeneity exists in the way image models are formulated, trained, and evaluated. As a consequence, direct comparison between image models is often difficult. This article reviews mostly known but often underappreciated properties relating to the evaluation and interpretation of generative models. In particular, we show that three of the currently most commonly used criteria---average log-likelihood, Parzen window estimates, and visual fidelity of samples---are largely independent of each other when images are high-dimensional. Good performance with respect to one criterion therefore need not imply good performance with respect to the other criteria. Our results show that extrapolation from one criterion to another is not warranted and generative models need to be evaluated directly with respect to the application(s) they were intended for. In addition, we provide examples demonstrating that Parzen window estimates should generally be avoided.

概率生成模型评估标准的独立性存在着许多亟需关注的性质，本文重点评估了影像模型，阐明对于高维数据当前最常用的三个标准，即平均对数似然、Parzen窗口估计和样本视觉保真度，这些几乎是相互独立的；因此，好的表现不一定意味着好的其他标准的表现，在评估生成模型的性能时需要针对其预期应用直接进行评估，并且通常应避免使用Parzen窗口方法。

生成模型评估注意事项