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CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information
[article]
2020
arXiv
pre-print
Mutual information (MI) minimization has gained considerable interests in various machine learning tasks. However, estimating and minimizing MI in high-dimensional spaces remains a challenging problem, especially when only samples, rather than distribution forms, are accessible. Previous works mainly focus on MI lower bound approximation, which is not applicable to MI minimization problems. In this paper, we propose a novel Contrastive Log-ratio Upper Bound (CLUB) of mutual information. We
arXiv:2006.12013v6
fatcat:ilsrj3qxsjerfgkqr5c7sq4nuq