  Generative adversarial networks (GANs) have been shown to provide an
effective way to model complex distributions and have obtained impressive
results on various challenging tasks. However, typical GANs require
fully-observed data during training. In this paper, we present a GAN-based
framework for learning from complex, high-dimensional incomplete data. The
proposed framework learns a complete data generator along with a mask generator
that models the missing data distribution. We further demonstrate how to impute
missing data by equipping our framework with an adversarially trained imputer.
We evaluate the proposed framework using a series of experiments with several
types of missing data processes under the missing completely at random
assumption.
