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Generative adversarial networks (GANs) have been shown to provide aneffective way to model complex distributions and have obtained impressiveresults on various challenging tasks. However, typical GANs requirefully-observed data during training. In this paper, we present a GAN-basedframework for learning from complex, high-dimensional incomplete data. Theproposed framework learns a complete data generator along with a mask generatorthat models the missing data distribution. We further demonstrate how to imputemissing data by equipping our framework with an adversarially trained imputer.We evaluate the proposed framework using a series of experiments with severaltypes of missing data processes under the missing completely at randomassumption.

MisGAN: Learning from Incomplete Data with Generative Adversarial Networks