Normalizing flows, which learn a distribution by transforming the data to samples from a Gaussian base distribution, have proven powerful density approximations. But their expressive power is limited by this choice of the base distribution. We, therefore, propose to generalize the base distribution to a more elaborate copula distribution to capture the properties of the target distribution more accurately. In a first empirical analysis, we demonstrate that this replacement can dramatically improve the vanilla normalizing flows in terms of flexibility, stability, and effectivity for heavy-tailed data. Our results suggest that the improvements are related to an increased local Lipschitz-stability of the learned flow.

翻译：标准化流通过将数据转换成高山基分布的样本来学习分配方法,这种流的正常化已证明具有强大的密度近似值。但是它们的表达力受到基分布选择的限制。因此,我们建议将基分布法推广到更精细的千叶分布法,以更准确地捕捉目标分布的特性。在第一次经验分析中,我们证明这种替代可以极大地改善大尾数据在灵活性、稳定性和效果方面的香草正常化流动。我们的结果显示,这些改进与当地知识流动的更稳定有关。