Density destructors are differentiable and invertible transforms that map multivariate PDFs of arbitrary structure (low entropy) into non-structured PDFs (maximum entropy). Multivariate Gaussianization and multivariate equalization are specific examples of this family, which break down the complexity of the original PDF through a set of elementary transforms that progressively remove the structure of the data. We demonstrate how this property of density destructive flows is connected to classical information theory, and how density destructors can be used to get more accurate estimates of information theoretic quantities. Experiments with total correlation and mutual information inmultivariate sets illustrate the ability of density destructors compared to competing methods. These results suggest that information theoretic measures may be an alternative optimization criteria when learning density destructive flows.

翻译：多变制和多变式均分是这个家庭的具体例子,它通过一套基本变换,逐渐去除数据结构,打破了原始PDF的复杂性。我们展示了密度破坏性流的这种特性如何与传统信息理论相联系,以及如何利用密度破坏性器来更准确地估计信息理论数量。在多变式组合中,完全相关和相互信息的实验显示了密度破坏性器相对于相互竞争的方法的能力。这些结果表明,在学习密度破坏性流动时,信息定理措施可能是一种替代性的优化标准。