We introduce a novel Mutual Information (MI) estimator that fundamentally reframes the discriminative approach. Instead of training a classifier to discriminate between joint and marginal distributions, we learn a normalizing flow that transforms one into the other. This technique produces a computationally efficient and precise MI estimate that scales well to high dimensions and across a wide range of ground-truth MI values.

翻译：我们提出了一种新颖的互信息估计器，它从根本上重构了判别式方法。该方法不再训练分类器来区分联合分布与边缘分布，而是学习一个将一种分布转换为另一种分布的正则化流。该技术能够产生计算效率高且精确的互信息估计，可良好地扩展至高维空间，并适用于广泛的真实互信息值范围。