We propose a novel approach to the problem of mutual information (MI) estimation via introducing normalizing flows-based estimator. The estimator maps original data to the target distribution with known closed-form expression for MI. We demonstrate that our approach yields MI estimates for the original data. Experiments with high-dimensional data are provided to show the advantages of the proposed estimator.

翻译：我们提出了一种通过引入基于归一化流的估计器来解决互信息（MI）估计问题的新方法。该估计器将原始数据映射到具有已知闭式互信息表达式的目标分布。我们证明该方法能够获得原始数据的互信息估计值。通过高维数据实验，展示了所提出估计器的优势。