ImprovingVariationalAutoencoderswithDensity Gap-based Regularization

Neural Information Processing Systems 

On that basis, we hypothesize that these two problems stem from the conflict between the KL regularization inELBo andthefunction definition oftheprior distribution. Assuch, wepropose a novel regularization to substitute the KL regularization in ELBo for VAEs, which isbased on the density gapbetween the aggregated posterior distribution and the prior distribution.

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