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A learned generative model often produces biased statistics relative to theunderlying data distribution. A standard technique to correct this bias isimportance sampling, where samples from the model are weighted by thelikelihood ratio under model and true distributions. When the likelihood ratiois unknown, it can be estimated by training a probabilistic classifier todistinguish samples from the two distributions. We employ this likelihood-freeimportance weighting method to correct for the bias in generative models. Wefind that this technique consistently improves standard goodness-of-fit metricsfor evaluating the sample quality of state-of-the-art deep generative models,suggesting reduced bias. Finally, we demonstrate its utility on representativeapplications in a) data augmentation for classification using generativeadversarial networks, and b) model-based policy evaluation using off-policydata.

Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting