Controlling Dependence in Implicit Generative Models via Spread Mutual Information

Abstract:Mutual information (MI) provides an objective for suppressing or encouraging statistical dependence in implicit generative models. However, direct MI evaluation is challenging in implicit models due to typically intractable densities. A remedy is estimating the generator gradient from the difference between conditional and marginal scores. This score difference can, in turn, be estimated by differentiating a log density ratio learned through classification. This construction nevertheless faces two difficulties: (i) singular distributions need not admit the required score functions, and (ii) poor overlap can hinder density-ratio estimation. We therefore introduce Spread Mutual Information (SMI), a weighted integral of MI across noise levels obtained by applying a common spreading kernel to the generated variable. Gaussian spreading yields smooth, strictly positive conditional and marginal densities, extending the gradient construction to distributions that may originally be singular. Across a variaty of experiments, SMI consistently achieves effective dependence control among MI-based methods and remains competitive with established task-specific approaches.

Machine Learning (stat.ML); Machine Learning (cs.LG)

arXiv:2610.10021 [stat.ML]

(or arXiv:2610.10021v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2610.10021 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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