FMMI: Flow Matching Mutual Information Estimation
Ivan Butakov ⋅ Alexander Semenenko ⋅ Valeriia Kirova ⋅ Ivan Oseledets ⋅ Alexey Frolov
Abstract
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.
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