Variational $f$-Divergence and Derangements for Discriminative Mutual Information Estimation

arXiv2305.20025MaRDI QIDQ6438701

Author name not available (Why is that?)

Publication date: 31 May 2023

Abstract: The accurate estimation of the mutual information is a crucial task in various applications, including machine learning, communications, and biology, since it enables the understanding of complex systems. High-dimensional data render the task extremely challenging due to the amount of data to be processed and the presence of convoluted patterns. Neural estimators based on variational lower bounds of the mutual information have gained attention in recent years but they are prone to either high bias or high variance as a consequence of the partition function. We propose a novel class of discriminative mutual information estimators based on the variational representation of the $f$-divergence. We investigate the impact of the permutation function used to obtain the marginal training samples and present a novel architectural solution based on derangements. The proposed estimator is flexible as it exhibits an excellent bias/variance trade-off. Experiments on reference scenarios demonstrate that our approach outperforms state-of-the-art neural estimators both in terms of accuracy and complexity.

Has companion code repository: https://github.com/tonellolab/fdime

This page was built for publication: Variational $f$-Divergence and Derangements for Discriminative Mutual Information Estimation

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6438701)