Recent work has shown that Neural Ordinary Differential Equations (ODEs) can serve as generative models of images using the perspective of Continuous Normalizing Flows (CNFs). Such models offer exact likelihood calculation, and invertible generation/density estimation. In this work we introduce a Multi-Resolution variant of such models (MRCNF), by characterizing the conditional distribution over the additional information required to generate a fine image that is consistent with the coarse image. We introduce a transformation between resolutions that allows for no change in the log likelihood. We show that this approach yields comparable likelihood values for various image datasets, with improved performance at higher resolutions, with fewer parameters, using only 1 GPU. Further, we examine the out-of-distribution properties of (Multi-Resolution) Continuous Normalizing Flows, and find that they are similar to those of other likelihood-based generative models.

翻译：最近的工作表明,神经普通差异(ODEs)可以使用连续正常流动(CNFs)的角度作为图像的基因模型。这些模型提供了精确的可能性计算和不可逆的生成/密度估计。在这项工作中,我们引入了这种模型的多分辨率变量(MRCNF),将有条件的分布与生成与粗糙图像相符合的美貌所需的额外信息相匹配。我们引入了分辨率之间的转换,不允许对日志进行任何改变。我们表明,这一方法为各种图像数据集得出了相似的概率值,高分辨率的性能得到改进,参数减少,仅使用1 GPU。此外,我们研究了(多分辨率)连续正常流动的分布特性,发现它们与其他基于概率的基因模型相似。