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

Summary The paper propose a method for correcting the bias in the outcomes of pretrained deep generative models. Given data from a generator distribution and the real distribution, the paper uses importance reweighting to up/down-weigh the generated samples. The importance weights are computed using a probabilistic binary classifier that predicts the identity of the data distribution. Experiments are shown on several tasks to show that the importance reweighting improves the task performance. The importance weighting using binary classification is a well-known technique.